The document provides a tutorial on using stratified k-fold cross-validation to design neural networks for regression problems. It presents a MATLAB code that performs 10-fold cross-validation on a neural network with 4 hidden nodes trained on a simple curve-fitting dataset. The code partitions the data into training, validation, and test sets for each fold, trains the network, and records performance metrics. Results showed average R^2 values greater than 0.95 across the folds.
Learning On The Border:Active Learning in Imbalanced classification Data萍華 楊
This paper proposes using active learning to address the problem of class imbalance in machine learning classification tasks. The key ideas are:
1) Active learning selects the most informative examples to label, which tend to be instances closest to the decision boundary. This helps provide a more balanced sample to the learner.
2) An online support vector machine (SVM) algorithm is used to allow efficient integration of newly labeled examples without retraining on the entire dataset.
3) Early stopping criteria based on support vectors are introduced to determine when enough examples have been labeled.
Empirical results on imbalanced datasets demonstrate that the active learning approach leads to improved classification performance compared to traditional supervised learning methods.
Two methods are described for optimizing cognitive model parameters: differential evolution (DE) and high-throughput computing with HTCondor. DE is a genetic algorithm that uses a population of models to explore the parameter space in parallel. It is well-suited for models with few parameters or short run times. HTCondor allows running a population of models over a computer network, making it suitable for larger, more complex models or simulating many participants. Examples of using each method with an ACT-R paired associate model are provided.
An Introduction to Simulation in the Social Sciencesfsmart01
This document provides an introduction to simulation design in the social sciences. It discusses why simulations are used, including to confirm theoretical results, explore unknown theoretical environments, and generate statistical estimates. It outlines the key stages of simulations, including specifying the model, assigning parameters, generating data, calculating and storing results, and repeating the process. Finally, it provides examples of simulations and discusses necessary programming tools and considerations for simulation design.
This document provides an introduction to MATLAB. It describes what MATLAB is, how to start and use the basic functions in MATLAB, and how to plot functions. MATLAB is a numerical computing environment and programming language. It is useful for matrix manipulations, solving equations, integration, and producing graphs. The document outlines how to perform calculations, define variables, create vectors and matrices, and plot functions in MATLAB. It provides examples of basic math operations, functions, plotting techniques, and accessing help in MATLAB.
This document discusses anomaly detection techniques. It defines anomaly detection as the identification of items, events or observations that do not conform to expected patterns in data mining. It then covers various anomaly detection methods including unsupervised, supervised and semi-supervised techniques. Specific algorithms discussed include LOF, RNN, and Twitter's Seasonal Hybrid ESD approach. Real-world applications of anomaly detection are also mentioned such as intrusion detection, fraud detection and system health monitoring.
Machine learning and linear regression programmingSoumya Mukherjee
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
Intro to Machine Learning for non-Data ScientistsParinaz Ameri
The document provides an overview of machine learning concepts including definitions, algorithms, and the machine learning pipeline. It discusses supervised and unsupervised learning algorithms like classification, regression, and clustering. It also describes steps in the machine learning pipeline such as data preparation, algorithm selection, model building, evaluation, and prediction. Examples of applications like spam filtering and recommendations are provided. The agenda outlines an introduction to machine learning algorithms and their implementation for different use cases.
The document provides a tutorial on using stratified k-fold cross-validation to design neural networks for regression problems. It presents a MATLAB code that performs 10-fold cross-validation on a neural network with 4 hidden nodes trained on a simple curve-fitting dataset. The code partitions the data into training, validation, and test sets for each fold, trains the network, and records performance metrics. Results showed average R^2 values greater than 0.95 across the folds.
Learning On The Border:Active Learning in Imbalanced classification Data萍華 楊
This paper proposes using active learning to address the problem of class imbalance in machine learning classification tasks. The key ideas are:
1) Active learning selects the most informative examples to label, which tend to be instances closest to the decision boundary. This helps provide a more balanced sample to the learner.
2) An online support vector machine (SVM) algorithm is used to allow efficient integration of newly labeled examples without retraining on the entire dataset.
3) Early stopping criteria based on support vectors are introduced to determine when enough examples have been labeled.
Empirical results on imbalanced datasets demonstrate that the active learning approach leads to improved classification performance compared to traditional supervised learning methods.
Two methods are described for optimizing cognitive model parameters: differential evolution (DE) and high-throughput computing with HTCondor. DE is a genetic algorithm that uses a population of models to explore the parameter space in parallel. It is well-suited for models with few parameters or short run times. HTCondor allows running a population of models over a computer network, making it suitable for larger, more complex models or simulating many participants. Examples of using each method with an ACT-R paired associate model are provided.
An Introduction to Simulation in the Social Sciencesfsmart01
This document provides an introduction to simulation design in the social sciences. It discusses why simulations are used, including to confirm theoretical results, explore unknown theoretical environments, and generate statistical estimates. It outlines the key stages of simulations, including specifying the model, assigning parameters, generating data, calculating and storing results, and repeating the process. Finally, it provides examples of simulations and discusses necessary programming tools and considerations for simulation design.
This document provides an introduction to MATLAB. It describes what MATLAB is, how to start and use the basic functions in MATLAB, and how to plot functions. MATLAB is a numerical computing environment and programming language. It is useful for matrix manipulations, solving equations, integration, and producing graphs. The document outlines how to perform calculations, define variables, create vectors and matrices, and plot functions in MATLAB. It provides examples of basic math operations, functions, plotting techniques, and accessing help in MATLAB.
This document discusses anomaly detection techniques. It defines anomaly detection as the identification of items, events or observations that do not conform to expected patterns in data mining. It then covers various anomaly detection methods including unsupervised, supervised and semi-supervised techniques. Specific algorithms discussed include LOF, RNN, and Twitter's Seasonal Hybrid ESD approach. Real-world applications of anomaly detection are also mentioned such as intrusion detection, fraud detection and system health monitoring.
Machine learning and linear regression programmingSoumya Mukherjee
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
Intro to Machine Learning for non-Data ScientistsParinaz Ameri
The document provides an overview of machine learning concepts including definitions, algorithms, and the machine learning pipeline. It discusses supervised and unsupervised learning algorithms like classification, regression, and clustering. It also describes steps in the machine learning pipeline such as data preparation, algorithm selection, model building, evaluation, and prediction. Examples of applications like spam filtering and recommendations are provided. The agenda outlines an introduction to machine learning algorithms and their implementation for different use cases.
This document discusses using MATLAB to test a Bayesian robust mixture model (BRMM) for clustering incomplete data with outliers. It generates synthetic data from a BRMM, corrupts the data by removing values and adding outliers, then estimates the BRMM hyperparameters to recover the original model. The BRMM is tested on synthetic data to demonstrate its capabilities in handling incomplete data with noise. Plots of the estimated model and boundaries are produced to evaluate the results.
This document introduces basic commands and concepts in Matlab, including how to define matrices and their elements, perform operations on matrices, and save workspaces. It provides examples of defining row vectors, column vectors, and multi-dimensional matrices. It also demonstrates using semicolons to define matrices that span multiple lines, line continuation with ellipses, and accessing individual matrix elements. Finally, it lists 10 practice problems for the reader to define matrices in Matlab and check their results.
Automated machine learning (AutoML) systems can find the optimal machine learning algorithm and hyperparameters for a given dataset without human intervention. AutoML addresses the skills gap in data science by allowing data scientists to build more models in less time. On average, tuning hyperparameters results in a 5-10% improvement in accuracy over default parameters. However, the best parameters vary across problems. AutoML tools like Auto-sklearn use techniques like Bayesian optimization and meta-learning to efficiently search the hyperparameter space. Auto-sklearn has won several AutoML challenges due to its ability to effectively optimize over 100 hyperparameters.
This project is based on programming with R. Use a huge dataset from Kaggle with purpose to predict the survival of the passengers of the Titanic with incomplete data.
This document provides an overview of how to build a basic neural network using Keras and TensorFlow. It discusses perceptrons and their limitations, the multilayer perceptron architecture, popular activation functions, and hyperparameters for regression and classification problems. It also covers saving and loading models, data augmentation techniques, and strategies for training deep neural networks.
Advanced MATLAB Tutorial for Engineers & ScientistsRay Phan
This is a more advanced tutorial in the MATLAB programming environment for upper level undergraduate engineers and scientists at Ryerson University. The first half of the tutorial covers a quick review of MATLAB, which includes how to create vectors, matrices, how to plot graphs, and other useful syntax. The next part covers how to create cell arrays, logical operators, using the find command, creating Transfer Functions, finding the impulse and step response, finding roots of equations, and a few other useful tips. The last part covers more advanced concepts such as analytically calculating derivatives and integrals, polynomial regression, calculating the area under a curve, numerical solutions to differential equations, and sorting arrays.
This document discusses various techniques for evaluating machine learning models and comparing their performance, including:
- Measuring error rates on separate test and training sets to avoid overfitting
- Using techniques like cross-validation, bootstrapping, and holdout validation when data is limited
- Comparing algorithms using statistical tests like paired t-tests
- Accounting for costs of different prediction outcomes in evaluation and model training
- Visualizing performance using lift charts and ROC curves to compare models
- The Minimum Description Length principle for selecting the model that best compresses the data
Leveraging Machine Learning or IA in order to detect Credit Card Fraud and suspicious transations. The aim of this presentation is to help you to improve your knowledge in Machnie Learning and to start development of multiple families of algorithms in Python.
Lab 2: Classification and Regression Prediction Models, training and testing ...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Lab 2: Classification and Regression Prediction Models, training and testing splits, optimization of K Nearest Neighbors (KD tree), optimization of Random Forest, optimization of Naive Bayes (Gaussian), advantages and model comparisons, feature importance, Feature ranking with recursive feature elimination, Two dimensional Linear Discriminant Analysis
It covers all the basics of MATLAB required for beginners. After going through these slides, anyone can write a MATLAB program and apply it to his field of interest.
This document provides information on generating and manipulating matrices in MATLAB. It discusses how to explicitly enter small matrices by separating elements with blanks/commas and rows with semicolons. Large matrices can be entered over multiple lines using the return button. Individual elements can be accessed and altered using indices in parentheses. Columns and rows can be appended to matrices using various commands. A colon is used to extract submatrices. Logical operators compare matrices and return 1s and 0s. Functions like diag(), fliplr(), flipud() and rot90() manipulate matrices. Random matrices can be generated using commands like rand() and ones(). Vectors subtracted from matrices using broadcasting. Example problems demonstrate generating matrices from vectors and manipulating the matrices.
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Matlab is a high-level programming language and environment used for numerical computation, visualization, and programming. The document outlines key Matlab concepts including the Matlab screen, variables, arrays, matrices, operators, plotting, flow control, m-files, and user-defined functions. Matlab allows users to analyze data, develop algorithms, and create models and applications.
This document provides an introduction to machine learning, including its roots in fields like information theory and artificial intelligence. It discusses the theoretical and empirical approaches to machine learning. Key terms are defined, such as instances (individual examples), features (characteristics of inputs), and greedy vs lazy learning algorithms. Greedy algorithms like decision trees abstract a model from data, while lazy algorithms like k-nearest neighbors retain all training data.
Steering Model Selection with Visual Diagnostics: Women in Analytics 2019Rebecca Bilbro
Machine learning is ultimately a search for the best combination of features, algorithm, and hyperparameters that result in the best performing model. Oftentimes, this leads us to stay in our algorithmic comfort zones, or to resort to automated processes such as grid searches and random walks. Whether we stick to what we know or try many combinations, we are sometimes left wondering if we have actually succeeded.
By enhancing model selection with visual diagnostics, data scientists can inject human guidance to steer the search process. Visualizing feature transformations, algorithmic behavior, cross-validation methods, and model performance allows us a peek into the high dimensional realm that our models operate. As we continue to tune our models, trying to minimize both bias and variance, these glimpses allow us to be more strategic in our choices. The result is more effective modeling, speedier results, and greater understanding of underlying processes.
Visualization is an integral part of the data science workflow, but visual diagnostics are directly tied to machine learning transformers and models. The Yellowbrick library extends the scikit-learn API providing a Visualizer object, an estimator that learns from data and produces a visualization as a result. In this tutorial, we will explore feature visualizers, visualizers for classification, clustering, and regression, as well as model analysis visualizers. We'll work through several examples and show how visual diagnostics steer model selection, making machine learning more informed, and more effective.
This document summarizes notes from a deep learning report and exercises. It discusses topics like input and hidden layers, activation functions, output layers, gradient descent, backpropagation, and solutions to problems like vanishing gradients. Key points covered include how neural networks transform input data through weighted layers, common activation functions for different layers, calculating error using loss functions, using gradient descent to minimize error by adjusting weights, and backpropagation to efficiently calculate gradients. Exercises reinforce understanding of these concepts through coding implementations and analyzing results.
This document provides an overview of machine learning techniques for classification and anomaly detection. It begins with an introduction to machine learning and common tasks like classification, clustering, and anomaly detection. Basic classification techniques are then discussed, including probabilistic classifiers like Naive Bayes, decision trees, instance-based learning like k-nearest neighbors, and linear classifiers like logistic regression. The document provides examples and comparisons of these different methods. It concludes by discussing anomaly detection and how it differs from classification problems, noting challenges like having few positive examples of anomalies.
Binary Class and Multi Class Strategies for Machine LearningPaxcel Technologies
This presentation discusses the following -
Possible strategies to follow when working on a new machine learning problem.
The common problems with classifiers (how to detect them and eliminate them).
Popular approaches on how to use binary classifiers to problems with multi class classification.
This document discusses machine learning concepts including tasks, experience, and performance measures. It provides definitions of machine learning from Arthur Samuel and Tom Mitchell. It describes common machine learning tasks like classification, regression, and clustering. It discusses supervised and unsupervised learning as experiences and provides examples of performance measures for different tasks. Finally, it provides an example of applying machine learning to the MNIST handwritten digit classification problem.
Classification using L1-Penalized Logistic RegressionSetia Pramana
L1-Penalized Logistic Regression, is commonly used for classification in high dimensional data such as microarray. This slide presents a brief overview of the algorithm.
This document provides an overview of machine learning concepts and code examples in Python. It discusses the typical 5 steps of machine learning projects: collaboration, data collection, clustering, classification, and conclusion. Code snippets demonstrate each step, including collecting data with Scrapy, clustering with k-means, classification with support vector machines, and evaluating results with a confusion matrix. Dimensionality reduction techniques like principal component analysis are also covered.
In this article you will learn hot to use tensorflow Softmax Classifier estimator to classify MNIST dataset in one script.
This paper introduces also the basic idea of a artificial neural network.
This document discusses using MATLAB to test a Bayesian robust mixture model (BRMM) for clustering incomplete data with outliers. It generates synthetic data from a BRMM, corrupts the data by removing values and adding outliers, then estimates the BRMM hyperparameters to recover the original model. The BRMM is tested on synthetic data to demonstrate its capabilities in handling incomplete data with noise. Plots of the estimated model and boundaries are produced to evaluate the results.
This document introduces basic commands and concepts in Matlab, including how to define matrices and their elements, perform operations on matrices, and save workspaces. It provides examples of defining row vectors, column vectors, and multi-dimensional matrices. It also demonstrates using semicolons to define matrices that span multiple lines, line continuation with ellipses, and accessing individual matrix elements. Finally, it lists 10 practice problems for the reader to define matrices in Matlab and check their results.
Automated machine learning (AutoML) systems can find the optimal machine learning algorithm and hyperparameters for a given dataset without human intervention. AutoML addresses the skills gap in data science by allowing data scientists to build more models in less time. On average, tuning hyperparameters results in a 5-10% improvement in accuracy over default parameters. However, the best parameters vary across problems. AutoML tools like Auto-sklearn use techniques like Bayesian optimization and meta-learning to efficiently search the hyperparameter space. Auto-sklearn has won several AutoML challenges due to its ability to effectively optimize over 100 hyperparameters.
This project is based on programming with R. Use a huge dataset from Kaggle with purpose to predict the survival of the passengers of the Titanic with incomplete data.
This document provides an overview of how to build a basic neural network using Keras and TensorFlow. It discusses perceptrons and their limitations, the multilayer perceptron architecture, popular activation functions, and hyperparameters for regression and classification problems. It also covers saving and loading models, data augmentation techniques, and strategies for training deep neural networks.
Advanced MATLAB Tutorial for Engineers & ScientistsRay Phan
This is a more advanced tutorial in the MATLAB programming environment for upper level undergraduate engineers and scientists at Ryerson University. The first half of the tutorial covers a quick review of MATLAB, which includes how to create vectors, matrices, how to plot graphs, and other useful syntax. The next part covers how to create cell arrays, logical operators, using the find command, creating Transfer Functions, finding the impulse and step response, finding roots of equations, and a few other useful tips. The last part covers more advanced concepts such as analytically calculating derivatives and integrals, polynomial regression, calculating the area under a curve, numerical solutions to differential equations, and sorting arrays.
This document discusses various techniques for evaluating machine learning models and comparing their performance, including:
- Measuring error rates on separate test and training sets to avoid overfitting
- Using techniques like cross-validation, bootstrapping, and holdout validation when data is limited
- Comparing algorithms using statistical tests like paired t-tests
- Accounting for costs of different prediction outcomes in evaluation and model training
- Visualizing performance using lift charts and ROC curves to compare models
- The Minimum Description Length principle for selecting the model that best compresses the data
Leveraging Machine Learning or IA in order to detect Credit Card Fraud and suspicious transations. The aim of this presentation is to help you to improve your knowledge in Machnie Learning and to start development of multiple families of algorithms in Python.
Lab 2: Classification and Regression Prediction Models, training and testing ...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Lab 2: Classification and Regression Prediction Models, training and testing splits, optimization of K Nearest Neighbors (KD tree), optimization of Random Forest, optimization of Naive Bayes (Gaussian), advantages and model comparisons, feature importance, Feature ranking with recursive feature elimination, Two dimensional Linear Discriminant Analysis
It covers all the basics of MATLAB required for beginners. After going through these slides, anyone can write a MATLAB program and apply it to his field of interest.
This document provides information on generating and manipulating matrices in MATLAB. It discusses how to explicitly enter small matrices by separating elements with blanks/commas and rows with semicolons. Large matrices can be entered over multiple lines using the return button. Individual elements can be accessed and altered using indices in parentheses. Columns and rows can be appended to matrices using various commands. A colon is used to extract submatrices. Logical operators compare matrices and return 1s and 0s. Functions like diag(), fliplr(), flipud() and rot90() manipulate matrices. Random matrices can be generated using commands like rand() and ones(). Vectors subtracted from matrices using broadcasting. Example problems demonstrate generating matrices from vectors and manipulating the matrices.
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Matlab is a high-level programming language and environment used for numerical computation, visualization, and programming. The document outlines key Matlab concepts including the Matlab screen, variables, arrays, matrices, operators, plotting, flow control, m-files, and user-defined functions. Matlab allows users to analyze data, develop algorithms, and create models and applications.
This document provides an introduction to machine learning, including its roots in fields like information theory and artificial intelligence. It discusses the theoretical and empirical approaches to machine learning. Key terms are defined, such as instances (individual examples), features (characteristics of inputs), and greedy vs lazy learning algorithms. Greedy algorithms like decision trees abstract a model from data, while lazy algorithms like k-nearest neighbors retain all training data.
Steering Model Selection with Visual Diagnostics: Women in Analytics 2019Rebecca Bilbro
Machine learning is ultimately a search for the best combination of features, algorithm, and hyperparameters that result in the best performing model. Oftentimes, this leads us to stay in our algorithmic comfort zones, or to resort to automated processes such as grid searches and random walks. Whether we stick to what we know or try many combinations, we are sometimes left wondering if we have actually succeeded.
By enhancing model selection with visual diagnostics, data scientists can inject human guidance to steer the search process. Visualizing feature transformations, algorithmic behavior, cross-validation methods, and model performance allows us a peek into the high dimensional realm that our models operate. As we continue to tune our models, trying to minimize both bias and variance, these glimpses allow us to be more strategic in our choices. The result is more effective modeling, speedier results, and greater understanding of underlying processes.
Visualization is an integral part of the data science workflow, but visual diagnostics are directly tied to machine learning transformers and models. The Yellowbrick library extends the scikit-learn API providing a Visualizer object, an estimator that learns from data and produces a visualization as a result. In this tutorial, we will explore feature visualizers, visualizers for classification, clustering, and regression, as well as model analysis visualizers. We'll work through several examples and show how visual diagnostics steer model selection, making machine learning more informed, and more effective.
This document summarizes notes from a deep learning report and exercises. It discusses topics like input and hidden layers, activation functions, output layers, gradient descent, backpropagation, and solutions to problems like vanishing gradients. Key points covered include how neural networks transform input data through weighted layers, common activation functions for different layers, calculating error using loss functions, using gradient descent to minimize error by adjusting weights, and backpropagation to efficiently calculate gradients. Exercises reinforce understanding of these concepts through coding implementations and analyzing results.
This document provides an overview of machine learning techniques for classification and anomaly detection. It begins with an introduction to machine learning and common tasks like classification, clustering, and anomaly detection. Basic classification techniques are then discussed, including probabilistic classifiers like Naive Bayes, decision trees, instance-based learning like k-nearest neighbors, and linear classifiers like logistic regression. The document provides examples and comparisons of these different methods. It concludes by discussing anomaly detection and how it differs from classification problems, noting challenges like having few positive examples of anomalies.
Binary Class and Multi Class Strategies for Machine LearningPaxcel Technologies
This presentation discusses the following -
Possible strategies to follow when working on a new machine learning problem.
The common problems with classifiers (how to detect them and eliminate them).
Popular approaches on how to use binary classifiers to problems with multi class classification.
This document discusses machine learning concepts including tasks, experience, and performance measures. It provides definitions of machine learning from Arthur Samuel and Tom Mitchell. It describes common machine learning tasks like classification, regression, and clustering. It discusses supervised and unsupervised learning as experiences and provides examples of performance measures for different tasks. Finally, it provides an example of applying machine learning to the MNIST handwritten digit classification problem.
Classification using L1-Penalized Logistic RegressionSetia Pramana
L1-Penalized Logistic Regression, is commonly used for classification in high dimensional data such as microarray. This slide presents a brief overview of the algorithm.
This document provides an overview of machine learning concepts and code examples in Python. It discusses the typical 5 steps of machine learning projects: collaboration, data collection, clustering, classification, and conclusion. Code snippets demonstrate each step, including collecting data with Scrapy, clustering with k-means, classification with support vector machines, and evaluating results with a confusion matrix. Dimensionality reduction techniques like principal component analysis are also covered.
In this article you will learn hot to use tensorflow Softmax Classifier estimator to classify MNIST dataset in one script.
This paper introduces also the basic idea of a artificial neural network.
This document provides a final report on a project to classify particle collision data from the Large Hadron Collider using machine learning models. In the first part, the author conducts exploratory data analysis on the training data, which has 250,000 examples and 33 features. Notable findings include some complete and relevant features, correlations between features, and imbalanced target classes. In the second part, the author experiments with various machine learning models, including KNN, logistic regression, decision trees, and gradient boosting models. LightGBM performs best, achieving 84% accuracy on a validation set. Hyperparameter tuning is then used to further improve LightGBM performance.
This document discusses machine learning techniques including linear support vector machines (SVMs), data splitting, model fitting and prediction, and histograms. It summarizes an SVM tutorial for predicting samples and evaluating models using classification reports and confusion matrices. It also covers kernel density estimation, PCA, and comparing different classifiers.
Introduction to use machine learning in python and pascal to do such a thing like train prime numbers when there are algorithms in place to determine prime numbers. See a dataframe, feature extracting and a few plots to re-search for another hot experiment to predict prime numbers.
Brief introduction of neural network including-
1. Fitting Tool
2. Clustering data with a self-organising map
3. Pattern Recognition Tool
4. Time Series Toolbox
This document discusses regularization and model selection techniques for machine learning models. It describes cross-validation methods like hold-out validation and k-fold cross validation that evaluate models on held-out data to select models that generalize well. Feature selection is discussed as an important application of model selection. Bayesian statistics and placing prior distributions on parameters is introduced as a regularization technique that favors models with smaller parameter values.
This document provides an introduction and overview of machine learning algorithms. It begins by discussing the importance and growth of machine learning. It then describes the three main types of machine learning algorithms: supervised learning, unsupervised learning, and reinforcement learning. Next, it lists and briefly defines ten commonly used machine learning algorithms including linear regression, logistic regression, decision trees, SVM, Naive Bayes, and KNN. For each algorithm, it provides a simplified example to illustrate how it works along with sample Python and R code.
interfacing matlab with embedded systemsRaghav Shetty
This Book is all about Interfacing Embedded System with Matlab. This book guides the beginners for creating GUI , Modeling with SimuLink & Interfacing Arduino , Raspberry Pi , BeagleBone with Embedded System. This Book is NOT FOR SALE , Only knowledge base for Open Source Community
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regre...Yao Yao
https://github.com/yaowser/data_mining_group_project
https://www.kaggle.com/c/zillow-prize-1/data
From the Zillow real estate data set of properties in the southern California area, conduct the following data cleaning, data analysis, predictive analysis, and machine learning algorithms:
Mini-lab 1: Stochastic Gradient Descent classifier, Optimizing Logistic Regression Model Performance, Optimizing Support Vector Machine Classifier, Accuracy of results and efficiency, Logistic Regression Feature Importance, interpretation of support vectors, Density Graph
A tour of the top 10 algorithms for machine learning newbiesVimal Gupta
The document summarizes the top 10 machine learning algorithms for machine learning newbies. It discusses linear regression, logistic regression, linear discriminant analysis, classification and regression trees, naive bayes, k-nearest neighbors, and learning vector quantization. For each algorithm, it provides a brief overview of the model representation and how predictions are made. The document emphasizes that no single algorithm is best and recommends trying multiple algorithms to find the best one for the given problem and dataset.
The document discusses various concepts in machine learning and deep learning including:
1. The semantic gap between what computers can see/read from raw inputs versus higher-level semantics. Deep learning aims to close this gap through hierarchical representations.
2. Traditional computer vision techniques versus deep learning approaches for tasks like face recognition.
3. The differences between rule-based AI, machine learning, and deep learning.
4. Key components of supervised machine learning models including data, models, loss functions, and optimizers.
5. Different problem types in machine learning like regression, classification, and their associated model architectures, activation functions, and loss functions.
6. Frameworks for machine learning like Keras and
This document discusses curve fitting in Matlab. It introduces the Curve Fitting tool and how to use it to fit data to functions. Key aspects covered include importing data into the tool, selecting fitting functions, viewing and analyzing fit results, and plotting residuals. Examples are provided of fitting data to a sine wave, linear function with and without weights, and examining fit confidence bounds and predictions over different ranges. The document provides a tutorial on using Matlab's Curve Fitting tool to model experimental data with functions.
This document provides instructions for two machine learning homework assignments involving time series prediction and classification. For the first assignment, students are asked to use neural networks to predict chaotic time series data from the Mackey-Glass equation, comparing performance of linear and nonlinear models. For the second assignment, students must classify iris flower types from the Iris data set using a neural network with four input nodes, three output nodes, and logistic output units, evaluating performance through cross-validation and testing.
How to make fewer errors at the stage of code writing. Part N3.PVS-Studio
This is the third article where I will tell you about a couple of new programming methods that can help you make your code simpler and safer. You may read the previous two posts here [1] and here [2]. This time we will take samples from the Qt project.
This document provides an introduction to object-oriented programming (OOP) in MATLAB. It discusses key OOP concepts like classes, objects, properties, and methods. It also demonstrates how to define a class in MATLAB, including specifying properties, methods, and inheritance from superclasses. Examples are provided of creating objects from classes and calling their methods.
This document provides an overview of clustering in machine learning. It discusses what clustering is, the different types of clustering including centroid-based, density-based, distribution-based, hierarchical, and grid-based clustering. It also provides examples of k-means clustering and discusses applications of clustering such as image recognition, biological research, and crime analysis.
The document describes the author's approach to building a machine learning pipeline for a Kaggle competition to predict product categories from tabular data. The pipeline includes: 1) Loading and processing the training, testing, and submission data, 2) Performing cross-validated model training and evaluation using algorithms like XGBoost, LightGBM and CatBoost, 3) Averaging the results to generate final predictions and create a submission file. The author aims to share details of algorithms, hardware performance, and results in subsequent blog posts.
The document discusses key concepts in neural networks including units, layers, batch normalization, cost/loss functions, regularization techniques, activation functions, backpropagation, learning rates, and optimization methods. It provides definitions and explanations of these concepts at a high level. For example, it defines units as the activation function that transforms inputs via a nonlinear function, and hidden layers as layers other than the input and output layers that receive weighted input and pass transformed values to the next layer. It also summarizes common cost functions, regularization approaches like dropout, and optimization methods like gradient descent and stochastic gradient descent.
This document provides an introduction to computer simulation. It discusses how simulation can be used to model real systems on a computer in order to understand system behavior and evaluate alternatives. It describes different types of models including iconic, symbolic, deterministic, stochastic, static, dynamic, continuous and discrete models. Monte Carlo simulation is introduced as a technique that uses random numbers. The document outlines the steps in a simulation study and provides examples of systems and their components that can be modeled using simulation.
Similar to Machine Learning Guide maXbox Starter62 (20)
In the last sessions we have seen that P4D (Python 4 Delphi) is powerful enough to offer components, Python packages or libraries in Delphi or Lazarus (FPC). This time we go the other way of usage and integration; how does the Python or web world in the shell benefit from the VCL components as GUI controls. We create a Python extension module from Delphi classes, packages or functions. Building Delphi’s VCL library as a specific Python module in a console or editor and launching a complete Windows GUI from a script can be the start of a long journey.
The flood of Open APIs is now so blatant that we take a closer look at some basics and principles. Of course, the best way to understand how APIs work is to try them. While most APIs require access via API keys or have complicated authentication and authorization methods, there are also open APIs with no requirements or licenses whatsoever. This is especially useful for beginners as we can start exploring different APIs right away. It’s also useful for web developers who want easy access to a sample dataset for their app; e.g. most weather apps get their weather forecast data from a weather API instead of building weather stations themselves.
Faker is a Python library that generates fake data. Fake data is often used for testing or filling databases with some dummy data. Faker is heavily inspired by PHP's Faker, Perl's Data::Faker, and by Ruby's Faker.
Many of the applications and organizations provide avatar features. Finally, synthetic datasets can minimize privacy concerns. Attempts to anonymize data can be ineffective, as even if sensitive/identifying variables are removed from the dataset
Python for Delphi (P4D) is a set of free components that wrap up the Python DLL into Delphi and Lazarus (FPC). They let you easily execute Python scripts, create new Python modules and new Python types. You can create Python extensions as DLLs and much more like scripting. P4D provides different levels of functionality: Low-level access to the python API High-level bi-directional interaction with Python Access to Python objects using Delphi custom variants (VarPyth.pas).
Python for Delphi (P4D) is a set of free components that wrap up the Python DLL into Delphi and Lazarus (FPC). They let you easily execute Python scripts, create new Python modules and new Python types. You can create Python extensions as DLLs and much more like scripting. P4D provides different levels of functionality:
Low-level access to the python API
High-level bi-directional interaction with Python
Access to Python objects using Delphi custom variants (VarPyth.pas)
Wrapping of Delphi objects for use in python scripts using RTTI (WrapDelphi.pas)
Creating python extension modules with Delphi classes and functions
Generate Scripts in maXbox from Python Installation
The document describes steps to build and train an image classification model using Lazarus, the neural-api library, and Google Colab. It clones the neural-api GitHub repository, installs dependencies like FPC and Lazarus, builds and trains a simple image classifier on the CIFAR-10 dataset, and exports the trained model weights and training logs. The process demonstrates how to leverage Google Colab's GPUs to train deep learning models using Lazarus and Pascal.
The portable pixmap format(PPM), the portable graymap format(PGM) and portable bitmap format(PBM) are image file formats designed to be easily exchanged between platforms. They are also sometimes referred collectively as the portable anymap format(PNM). These formats are a convenient (simple) method of saving image data. And the format is not even limited to graphics, its definition allowing it to be used for arbitrary three-dimensional matrices or cubes of unsigned integers.
This tutor puts a trip to the kingdom of object recognition with computer vision knowledge and an image classifier.
Object detection has been witnessing a rapid revolutionary change in some fields of computer vision. Its involvement in the combination of object classification
as well as object recognition makes it one of the most challenging topics in the domain of machine learning & vision.
How can we visualize data in machine learning with VS Code? This is a C# wrapper for the GraphViz graph generator for dotnet core. Further bindings for Python GraphViz are shown and exports to MS Power BI all in MS Visual Code, Jupyter and dotnet core.
K-CAI NEURAL API is a Keras based neural network API for machine learning that will allow you to prototype with a lots of possibilities of Tensorflow! Python, Free Pascal and Delphi together in Google Colab, Git or the Community Edition.
Software is changing the world. CGC is a Common Gateway Coding as the name says, it is a "common" language approach for almost everything. I want to show how a multi-language approach to infrastructure as code using general purpose programming languages lets cloud engineers and code producers unlocking the same software engineering techniques commonly used for applications.
Code Review Checklist: How far is a code review going? "Metrics measure the design of code after it has been written, a Review proofs it and Refactoring improves code."
In this paper a document structure is shown and tips for a code review.
Some checks fits with your existing tools and simply raises a hand when the quality or security of your codebase is impaired.
Open LDAP as A directory serviceis a system for storing and retrieving information in a tree-like structure with the following key properties:
Optimized for reading Distributed storage model Extensible data storage types Advanced search capabilities Consistent replication possibilities
This document discusses closures and functional programming. It begins with an agenda that covers closures as code blocks, their history in languages like Lisp and Scheme, examples of functional programming, and using closures for refactoring. It then discusses a case study on experiences with a polygraph design, including optimizations with closures, packaging, and applying the Demeter principle. Finally, it provides links for further reading on closures.
This document explains how to redirect console output from a GUI application to the parent command prompt process. It describes using the AttachConsole and FreeConsole functions to attach and detach a process from the console. The GetParentProcessName function is used to get the name of the parent process (e.g. cmd.exe or powershell.exe) to determine if output should be redirected. The code sample shows attaching the console, writing sample output, and detaching when complete.
This tutor shows the train and test set split with binary classifying, clustering and 3D plots and discuss a probability density function in scikit-learn on synthetic datasets. The dataset is very simple as a reference of understanding.
The term “machine learning” is used to describe one kind of “artificial intelligence” (AI) where a machine is able to learn and adapt through its own experience. We crawled and collected 30 top overview diagrams which shows the topic of methods, algorithms and concepts.
TensorFlow is a Python-friendly open source library for numerical computation that makes machine learning faster and easier and ease the process of acquiring data, training models, serving predictions, and refining future results.
In this session Max Kleiner shows four groups of the ML: Regression, Dimension Reduction, Clustering and Classification. ML recognizes patterns and laws in the learning data. Most ML projects allegedly fail due to lack of data consolidation and due to lack of hypothesis. On the basis of the well-known IRIS dataset the 4 groups with 4 algorithms each are gone through and this lack is avoided.
TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
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Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
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The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
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In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
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Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
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Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
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If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Odoo ERP software
Odoo ERP software, a leading open-source software for Enterprise Resource Planning (ERP) and business management, has recently launched its latest version, Odoo 17 Community Edition. This update introduces a range of new features and enhancements designed to streamline business operations and support growth.
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1. ////////////////////////////////////////////////////////////////////////////
Machine Learning
____________________________________________________________________________
maXbox Starter 60 II - Data Science with ML.
"In the face of ambiguity, refuse the temptation to guess."
– The Zen of Python
This tutor introduces the basic idea of machine learning with a very simple
example. Machine learning teaches machines (and me too) to learn to carry out
tasks and concepts by themselves. It is that simple, so here is an overview:
http://www.softwareschule.ch/examples/machinelearning.jpg
Of course, machine learning (often also referred to as Artificial Intelligence,
Artificial Neural Network, Big Data, Data Mining or Predictive Analysis) is not
that new field in itself as they want to believe us. For most of the cases you
do experience 5 steps in different loops:
• Collab (Set a thesis, understand the data, get resources)
• Collect (Scrapy data, store, filter and explore data)
• Cluster (Choosing a model and category algorithm - unsupervised)
• Classify (Choosing a model and classify algorithm - supervised)
• Conclude (Predict or report context and drive data to decision)
For example, say your business needs to adopt a new technology in Sentiment
Analysis and there’s a shortage of experienced candidates who are qualified to
fill the relevant positions (also known as a skills gap).
You can also skip collecting data by your own and expose the topic straight to
an Internet service API like REST to forward clustered data traffic directly to
your server being accessed. How important collect, cluster and classify is
points out next 3 definitions;
"Definition: Digital Forensic - to collect evidence.
" Taxonomy - to classify things.
" Deep Learning - to compute many hidden layers.
At its core, most algorithms should have a proof of classification and this is
nothing more than keeping track of which feature gives evidence to which class.
The way the features are designed determines the model that is used to learn.
This can be a confusion matrix, a certain confidence interval, a T-Test
statistic, p-value or something else used in hypothesis1
testing.
http://www.softwareschule.ch/examples/decision.jpg
Lets start with some code snippets to grape the 5 steps, assuming that you have
Python or maXbox already installed (everything at least as recent as 2.7 should
be fine or better 3.6 as we do), we need to install NumPy and SciPy for
numerical operations, as well as matplotlib and sklearn for visualization:
"Collaboration"
1 A thesis with evidence
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2. import itertools
import numpy as np
import matplotlib.pyplot as plt
import maxbox as mx
from sklearn.decomposition import PCA
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from scipy import special, optimize
Then we go like this 5 steps:
• Collab (Python and maXbox as a tool)
• Collect (from scrapy.crawler import CrawlerProcess)
• Cluster (clustering with K-Means - unsupervised)
• Classify (classify with Support Vector Machines - supervised)
• Conclude (test with a Confusion Matrix)
"Collecting"
class BlogSpider(scrapy.Spider):
name = 'blogspider'
start_urls = ['https://blog.scrapinghub.com']
def parse(self, response):
for title in response.css('h2.entry-title'):
yield {'title': title.css('a ::text').extract_first()}
for next_page in response.css('div.prev-post > a'):
yield response.follow(next_page, self.parse)
print(next_page)
We are going to create a class called LinkParser that inherits some methods from
HTMLParser which is why it is passed into the definition. This snippet can be
used to run scrapy spiders independent of scrapyd or the scrapy command line
tool and use it from a script.
2/8
3. "Clustering"
def createClusteredData(N, k):
pointsPerCluster = float(N)/k
X = []
y = []
for i in range (k):
incomeCentroid = np.random.uniform(20000.0, 200000.0)
ageCentroid = np.random.uniform(20.0, 70.0)
for j in range(int(pointsPerCluster)):
X.append([np.random.normal(incomeCentroid, 10000.0),
np.random.normal(ageCentroid, 2.0)])
y.append(i)
X = np.array(X)
y = np.array(y)
print('Cluster uniform, with normalization')
print(y)
return X, y
The 2 arrays you can see is X as the feature array and y as the predict array
(array object as a list)! We create a fake income / age clustered data that we
use for our K-Means clustering example above for the simplicity.
"Classification"
Now we will use linear SVC to partition our graph into clusters and split the
data into a training set and a test set for further predictions.
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
3/8
4. # Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)
By setting up a dense mesh of points in the grid and classifying all of them, we
can render the regions of each cluster as distinct colors:
def plotPredictions(clf):
xx, yy = np.meshgrid(np.arange(0, 250000, 10),
np.arange(10, 70, 0.5))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
plt.figure(figsize=(8, 6))
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
plt.scatter(X[:,0], X[:,1], c=y.astype(np.float))
plt.show()
It returns coordinate matrices from coordinate vectors. Make N-D coordinate
arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids,
given one-dimensional coordinate arrays x1, x2,..., xn.
Or just use predict for a given point:
print(svc.predict([[100000, 60]]))
print(svc.predict([[50000, 30]]))
You should choose to ask a more meaningful question. Without some context,
you might as well flip a coin.
4/8
5. "Conclusion"
The last step as an example of confusion matrix usage to evaluate the quality of
the output on the data set. The diagonal elements represent the number of points
for which the predicted label is equal to the true label, while off-diagonal
elements are those that are mislabeled by the classifier. The higher the
diagonal values of the confusion matrix the better, indicating many correct
predictions.
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
Write some code, run some tests, fiddle with features, run a test, fiddle with
features, realize everything is slow, and decide to use more layers or factors.
5/8
6. "Comprehension"
A last point is dimensionality reduction as the plot on
http://www.softwareschule.ch/examples/machinelearning.jpg shows, its more a
preparation but could also necessary to data reduction or to find a thesis.
Principal component analysis (PCA) is often the first thing to try out if you
want to cut down number of features and do not know what feature extraction
method to use.
PCA is limited as its a linear method, but chances are that it already goes far
enough for your model to learn well enough.
Add to this the strong mathematical properties it offers and the speed at which
it finds the transformed feature data space and is later able to transform
between original and transformed features; we can almost guarantee that it also
will become one of your frequently used machine learning tools.
This tutor will go straight to an overview to PCA.
The script 811_mXpcatest_dmath_datascience.pas (pcatest.pas) (located in the
democonsolecurfit subdirectory) performs a principal component analysis on a
set of 4 variables. Summarizing it, given the original feature space, PCA finds
a linear projection of itself in a lower dimensional space that has the
following two properties:
• The conserved variance is maximized.
• The final reconstruction error (when trying to go back from transformed
features to the original ones) is minimized.
As PCA simply transforms the input data, it can be applied both to
classification and regression problems. In this section, we will use a
classification task to discuss the method.
6/8
7. The script can be found at:
http://www.softwareschule.ch/examples/811_mXpcatest_dmath_datascience.pas
..examples811_mXpcatest_dmath_datascience.pas
It may be seen that:
• High correlations exist between the original variables, which are
therefore not independent
• According to the eigenvalues, the last two principal factors may be
neglected since they represent less than 11 % of the total variance. So,
the original variables depend mainly on the first two factors
• The first principal factor is negatively correlated with the second and
fourth variables, and positively correlated with the third variable
• The second principal factor is positively correlated with the first
variable
• The table of principal factors show that the highest scores are usually
associated with the first two principal factors, in agreement with the
previous results
Const
N = 11; { Number of observations }
Nvar = 4; { Number of variables }
Of course, its not always this and that simple. Often, we dont know what number
of dimensions is advisable in upfront. In such a case, we leave n_components or
Nvar parameter unspecified when initializing PCA to let it calculate the full
transformation. After fitting the data, explained_variance_ratio_ contains an
array of ratios in decreasing order: The first value is the ratio of the basis
vector describing the direction of the highest variance, the second value is the
ratio of the direction of the second highest variance, and so on.
Being a linear method, PCA has, of course, its limitations when we are faced
with strange data that has non-linear relationships. We wont go into much more
details here, but its sufficient to say that there are extensions of PCA.
Ref:
Building Machine Learning Systems with Python
Second Edition March 2015
DMath Math library for Delphi, FreePascal and Lazarus May 14, 2011
http://www.softwareschule.ch/box.htm
http://fann.sourceforge.net
http://neuralnetworksanddeeplearning.com/chap1.html
http://people.duke.edu/~ccc14/pcfb/numpympl/MatplotlibBarPlots.html
Doc:
Neural Networks Made Simple: Steffen Nissen
http://fann.sourceforge.net/fann_en.pdf
http://www.softwareschule.ch/examples/datascience.txt
https://maxbox4.wordpress.com
https://www.tensorflow.org/
7/8
8. https://sourceforge.net/projects/maxbox/files/Examples/13_General
/811_mXpcatest_dmath_datascience.pas/download
https://sourceforge.net/projects/maxbox/files/Examples/13_General
/809_FANN_XorSample_traindata.pas/download
https://stackoverflow.com/questions/13437402/how-to-run-scrapy-from-within-a-
python-script
Plots displaying the explained variance over the number of components is called
a Scree plot. A nice example of combining a Screeplot with a grid search to find
the best setting for the classification problem can be found at
http://scikit-learn.sourceforge.net/stable/auto_examples/plot_digits_pipe.html.
Although, PCA tries to use optimization for retained variance, multidimensional
scaling (MDS) tries to retain the relative distances as much as possible when
reducing the dimensions. This is useful when we have a high-dimensional dataset
and want to get a visual impression.
Machine learning is the science of getting computers to act without being
explicitly programmed. In the past decade, machine learning has given us self-
driving cars, practical speech recognition, effective web search, and a vastly
improved understanding of the human genome. Machine learning is so pervasive
today that you probably use it dozens of times a day without knowing it.
>>> Building Machine Learning Systems with Python
>>> Second Edition
ValueError: The truth value of an array with more than one element is ambiguous. Use
a.any() or a.all()
ypeError: data type not understood
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
HAMLET, PRINCE OF DENMARK by William Shakespeare
PERSONS REPRESENTED.
Claudius, King of Denmark.
Hamlet, Son to the former, and Nephew to the present King.
Polonius, Lord Chamberlain.
Horatio, Friend to Hamlet.
Laertes, Son to Polonius.
Voltimand, Courtier.
Cornelius, Courtier.
Rosencrantz, Courtier.
Guildenstern, Courtier.
Osric, Courtier.
A Gentleman, Courtier.
A Priest.
Marcellus, Officer.
Bernardo, Officer.
Francisco, a Soldier
Reynaldo, Servant to Polonius.
Players.
Two Clowns, Grave-diggers.
Fortinbras, Prince of Norway.
A Captain.
English Ambassadors.
Ghost of Hamlet's Father.
Gertrude, Queen of Denmark, and Mother of Hamlet.
Ophelia, Daughter to Polonius.
Lords, Ladies, Officers, Soldiers, Sailors, Messengers, and other
Attendants.
SCENE. Elsinore.
8/8