Gradient boosted trees are an ensemble machine learning technique that produces a prediction model as an ensemble of weak prediction models, typically decision trees. It builds models sequentially to minimize a loss function using gradient descent. Each new model is fit to the negative gradient of the loss function to reduce error. This allows weak learners to be combined into a stronger learner with better predictive performance than a single decision tree. Key advantages are it is fast, easy to tune, and achieves good performance.
The document provides an overview of concepts and topics to be covered in the MIS End Term Exam for AI and A2 on February 6th 2020, including: decision trees, classifier algorithms like ID3, CART and Naive Bayes; supervised and unsupervised learning; clustering using K-means; bias and variance; overfitting and underfitting; ensemble learning techniques like bagging and random forests; and the use of test and train data.
1) Machine learning involves analyzing data to find patterns and make predictions. It uses mathematics, statistics, and programming.
2) Key aspects of machine learning include understanding the business problem, collecting and preparing data, building and evaluating models, and different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
3) Common machine learning algorithms discussed include linear regression, logistic regression, KNN, K-means clustering, decision trees, and handling issues like missing values, outliers, and feature engineering.
Ensemble methods like bagging, boosting, random forest and AdaBoost combine multiple classifiers to improve performance. Bagging aims to reduce variance by training classifiers on random subsets of data and averaging their predictions. Boosting sequentially trains classifiers to focus on misclassified examples from previous classifiers to reduce bias. Random forest extends bagging by randomly selecting features for training each decision tree. AdaBoost is a boosting algorithm that iteratively adds classifiers and assigns higher weights to misclassified examples.
Machine learning is a type of artificial intelligence that allows software to learn from data without being explicitly programmed. The document discusses several machine learning techniques including supervised learning algorithms like linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, and Naive Bayes. Unsupervised learning algorithms covered include clustering techniques like K-means and hierarchical clustering. Applications of machine learning include spam filtering, fraud detection, image recognition, and medical diagnosis.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
The document provides an introduction to supervised learning. It discusses how supervised learning models are trained on labelled datasets containing both input data and corresponding results or labels. The model learns from these examples to predict accurate results for new, unseen data. Common applications of supervised learning mentioned include sentiment analysis, recommendations, and spam filtration. Decision trees and K-nearest neighbors are discussed as examples of supervised learning algorithms. Decision trees use a top-down approach to split the dataset into more homogeneous subsets. K-nearest neighbors classifies new data based on similarity to labelled examples in the training set.
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
The document provides an overview of concepts and topics to be covered in the MIS End Term Exam for AI and A2 on February 6th 2020, including: decision trees, classifier algorithms like ID3, CART and Naive Bayes; supervised and unsupervised learning; clustering using K-means; bias and variance; overfitting and underfitting; ensemble learning techniques like bagging and random forests; and the use of test and train data.
1) Machine learning involves analyzing data to find patterns and make predictions. It uses mathematics, statistics, and programming.
2) Key aspects of machine learning include understanding the business problem, collecting and preparing data, building and evaluating models, and different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
3) Common machine learning algorithms discussed include linear regression, logistic regression, KNN, K-means clustering, decision trees, and handling issues like missing values, outliers, and feature engineering.
Ensemble methods like bagging, boosting, random forest and AdaBoost combine multiple classifiers to improve performance. Bagging aims to reduce variance by training classifiers on random subsets of data and averaging their predictions. Boosting sequentially trains classifiers to focus on misclassified examples from previous classifiers to reduce bias. Random forest extends bagging by randomly selecting features for training each decision tree. AdaBoost is a boosting algorithm that iteratively adds classifiers and assigns higher weights to misclassified examples.
Machine learning is a type of artificial intelligence that allows software to learn from data without being explicitly programmed. The document discusses several machine learning techniques including supervised learning algorithms like linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, and Naive Bayes. Unsupervised learning algorithms covered include clustering techniques like K-means and hierarchical clustering. Applications of machine learning include spam filtering, fraud detection, image recognition, and medical diagnosis.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
The document provides an introduction to supervised learning. It discusses how supervised learning models are trained on labelled datasets containing both input data and corresponding results or labels. The model learns from these examples to predict accurate results for new, unseen data. Common applications of supervised learning mentioned include sentiment analysis, recommendations, and spam filtration. Decision trees and K-nearest neighbors are discussed as examples of supervised learning algorithms. Decision trees use a top-down approach to split the dataset into more homogeneous subsets. K-nearest neighbors classifies new data based on similarity to labelled examples in the training set.
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
Supervised learning is a machine learning approach that's defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule analysis, and ensemble methods like bagging and boosting. It also discusses key considerations for classification and prediction like accuracy, speed, robustness, and scalability. The goal is to construct models or classifiers that can predict categorical labels for classification tasks or continuous values for prediction tasks.
Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Mai...ijistjournal
Machine learning [1] is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled training data to infer a function that maps inputs to outputs, unsupervised learning looks for hidden patterns in unlabeled data, and reinforcement learning allows an agent to learn from interaction with an environment through trial-and-error using feedback in the form of rewards. Some common machine learning algorithms include support vector machines, discriminant analysis, naive Bayes classification, and k-means clustering.
The document is an internship report submitted by Amit Kumar to Persistent System Limited detailing work done to classify handwritten digits using machine learning algorithms. It provides an overview of tasks completed including understanding the problem and data, building a random forest model to classify digits, and evaluating the model's performance. Multiple models were created using random samples of the training data and results were aggregated to validate the overall accuracy of the digit classification.
The document provides an introduction to data mining and knowledge discovery. It discusses how large amounts of data are extracted and transformed into useful information for applications like market analysis and fraud detection. The key steps in the knowledge discovery process are described as data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Common data sources, database architectures, and types of coupling between data mining systems and databases are also outlined.
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Mai...ijistjournal
Machine learning [1] is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
An Introduction to Random Forest and linear regression algorithmsShouvic Banik0139
This presentation aims to provide a comprehensive understanding of the Random Forest and Linear Regression algorithms, their functioning, and significance. It is designed to equip the audience with the knowledge required to apply these algorithms effectively in practical scenarios, and to further enhance their expertise in the field.
The document discusses various machine learning algorithms and libraries in Python. It provides descriptions of popular libraries like Pandas for data analysis and Seaborn for data visualization. It also summarizes commonly used algorithms for classification and regression like random forest, support vector machines, neural networks, linear regression, and logistic regression. Additionally, it covers model evaluation metrics, pre-processing techniques, and the process of model selection.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
The document discusses several practical issues in learning decision trees: 1) determining the depth to grow the tree to avoid overfitting, 2) handling continuous attributes, 3) choosing an appropriate attribute selection measure, 4) handling missing attribute values, and 5) handling attributes with differing costs. It also discusses techniques for avoiding overfitting like pre-pruning and post-pruning trees as well as reduced error pruning and rule post-pruning.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Supervised learning is a machine learning approach that's defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately.
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule mining, and ensemble methods like bagging and boosting. Classification involves predicting categorical labels while prediction predicts continuous values. Key steps for preparing data include cleaning, transformation, and comparing different methods based on accuracy, speed, robustness, scalability, and interpretability.
This document discusses various machine learning techniques for classification and prediction. It covers decision tree induction, tree pruning, Bayesian classification, Bayesian belief networks, backpropagation, association rule analysis, and ensemble methods like bagging and boosting. It also discusses key considerations for classification and prediction like accuracy, speed, robustness, and scalability. The goal is to construct models or classifiers that can predict categorical labels for classification tasks or continuous values for prediction tasks.
Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Mai...ijistjournal
Machine learning [1] is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled training data to infer a function that maps inputs to outputs, unsupervised learning looks for hidden patterns in unlabeled data, and reinforcement learning allows an agent to learn from interaction with an environment through trial-and-error using feedback in the form of rewards. Some common machine learning algorithms include support vector machines, discriminant analysis, naive Bayes classification, and k-means clustering.
The document is an internship report submitted by Amit Kumar to Persistent System Limited detailing work done to classify handwritten digits using machine learning algorithms. It provides an overview of tasks completed including understanding the problem and data, building a random forest model to classify digits, and evaluating the model's performance. Multiple models were created using random samples of the training data and results were aggregated to validate the overall accuracy of the digit classification.
The document provides an introduction to data mining and knowledge discovery. It discusses how large amounts of data are extracted and transformed into useful information for applications like market analysis and fraud detection. The key steps in the knowledge discovery process are described as data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. Common data sources, database architectures, and types of coupling between data mining systems and databases are also outlined.
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
Implementation of Naive Bayesian Classifier and Ada-Boost Algorithm Using Mai...ijistjournal
Machine learning [1] is concerned with the design and development of algorithms that allow computers to evolve intelligent behaviors based on empirical data. Weak learner is a learning algorithm with accuracy less than 50%. Adaptive Boosting (Ada-Boost) is a machine learning algorithm may be used to increase accuracy for any weak learning algorithm. This can be achieved by running it on a given weak learner several times, slightly alters data and combines the hypotheses. In this paper, Ada-Boost algorithm is used to increase the accuracy of the weak learner Naïve-Bayesian classifier. The Ada-Boost algorithm iteratively works on the Naïve-Bayesian classifier with normalized weights and it classifies the given input into different classes with some attributes. Maize Expert System is developed to identify the diseases of Maize crop using Ada-Boost algorithm logic as inference mechanism. A separate user interface for the Maize expert system consisting of three different interfaces namely, End-user/farmer, Expert and Admin are presented here. End-user/farmer module may be used for identifying the diseases for the symptoms entered by the farmer. Expert module may be used for adding rules and questions to data set by a domain expert. Admin module may be used for maintenance of the system.
An Introduction to Random Forest and linear regression algorithmsShouvic Banik0139
This presentation aims to provide a comprehensive understanding of the Random Forest and Linear Regression algorithms, their functioning, and significance. It is designed to equip the audience with the knowledge required to apply these algorithms effectively in practical scenarios, and to further enhance their expertise in the field.
The document discusses various machine learning algorithms and libraries in Python. It provides descriptions of popular libraries like Pandas for data analysis and Seaborn for data visualization. It also summarizes commonly used algorithms for classification and regression like random forest, support vector machines, neural networks, linear regression, and logistic regression. Additionally, it covers model evaluation metrics, pre-processing techniques, and the process of model selection.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
Data mining involves using algorithms to find patterns in large datasets. It is commonly used in market research to perform tasks like classification, prediction, and association rule mining. The document discusses several common data mining techniques like decision trees, naive Bayes classification, and regression trees. It also covers related topics like cross-validation, bagging, and boosting methods used for improving model performance.
The document discusses several practical issues in learning decision trees: 1) determining the depth to grow the tree to avoid overfitting, 2) handling continuous attributes, 3) choosing an appropriate attribute selection measure, 4) handling missing attribute values, and 5) handling attributes with differing costs. It also discusses techniques for avoiding overfitting like pre-pruning and post-pruning trees as well as reduced error pruning and rule post-pruning.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
2. Data Mining
Data Mining : It is a process of extracting patterns from data. They should be:
Valid: holding on to new data with some certainity
Novel: being non-obvious to the system.
Useful: should be possible to act on the item
Understandable: Humans should be able to interpret the pattern.
Also known as Knowledge Discovery in Databases (KDD).
3. Data Mining might mean:
Statistics Visualizatiom
Artificial
Intelligence
Database
Technology
Machine Learning Neural Networks
Information
Retreival
Knowledge-based
systems
Knowledge
acquisition
Pattern
Recognition
High performance
computing
And so on….
4. What's needed?
Suitable data Computing power Data mining software
Someone who knows both
the nature of data and the
software tools.
Reason, theory or hunch
5. Typical
applications of
Data Mining
and KDD
Data Mining and KDD have
widespread applications.
Some examples include: Marketing
Healthcare Financial services And so on….
6. Some basic techniques
Predictive model: It basically describes what will happen in the future,rather predicts by
analyzing the given current data. It uses statistical analysis, machine learning algorithms and
other forecast techniques to predict what might happen in the future.It is not accurate as it is
essentially just a prediction into the future using the data and the given stastistical/Machine
Learning techniques. Eg- Performance Analysis.
Descriptive model: It basically gives a vision into the past and tells what exactly happened in
the past. It involves Data Aggregation and Data Mining.It is accurate as it describes exactly
what happened in the past. Eg- Sentiment Analysis.
Prescriptive model: This is realtively new field in Data Science.It is a step above predictive
and descriptive model. It basically provides a viable solution to the problem in hand and the
impact of considering a solution on future trend.It is still an evolving technique. Eg- Google
self driving car.
7. Some basic techniques
Predictive
Regression
Classification
Collaborative Filtering
Descriptive
Clustering
Association rules and variants
Deviation detection
8. Key data mining tasks
Classification: mapping
data into predefined
groups or classes.
Regression: mapping data
item to a real valued
prediction variable.
Clustering: Grouping
similar data together into
clusters.
9. Key learning tasks in Machine Learning
Supervised learning: A set of well-labled
data is given with defined inputs and
outputs variables (training data ) and the
algorithms learn to predict the output
from the input data.
Unsupervised learning: Data given is not
labelled ie. only input variables are given
with no corresponding output variables.
The algorithms find patterns and draw
inferences from the given data. This is
"pure Data Mining".
Semi-supervised: Some data is labeled
but most of it is unlabeled and a mixture
of supervised and unsupervised
techniques can be used.
10. Some basic Data Mining Methods
Decision Trees Neural Networks
Cluster/Nearest
Neighbour
Genetic
Algorithms/Evolutionary
Computing
Bayesien Networks Statistics Hybrids
11. Gradient
boosted trees
We are interested in Gradient boosted trees.
We would use Rapidminer (possibly Python?)
12. Gradient boosted trees
Decision Trees
We will discuss a bit about decision trees first.
A decision tree is a tree where each node represents a feature(attribute), each
link(branch) represents a decision(rule) and each leaf represents an
outcome(categorical or continues value).
A decision tree takes a set of input features and splits input data recursively based
on those features.
The processes are repeated until some stop condition is met. Ex- Depth of tree, no
more information gain possible etc.
13. Gradient boosted trees
Decision Trees have been there for a long time and have also known to suffer from
bias and variance.
We have a large bias with simple trees and large variance with complex trees.
Ensemble methods combine several decision trees to produce better predictive
performance rather than utilizing a single decision tree.
The main principle behind the ensemble model is that a group of weak learners
come together to form a strong learner.
A few ensemble methods : Bagging, Boosting
We will see each of them.
14. Gradient boosted trees
Bagging
It's used when our goal is to reduce the variance of the decision tree.
Here the idea is to take a súbset of data from training sample chosen randomly
with replacement.
Now, each collection of subset data is used to train their decision trees.
Thus we end up with ensemble of different models and their average is much more
robust than a single decision tree,which is much more robust in Predictive
Analysis.
Random Forest is an extension of Bagging.
15. Gradient boosted trees
Random Forest
It is basically a collection or ensemble of model of numerous decision trees. A collection of
trees is generally called forest.
It is also a bagging technique with a key difference, it takes a subset of features at each split
, and prune the trees with a stopping criteria for node splits.
The tree is grown to the largest.
The above steps are repeated and the prediction is given based on the aggregation of
predictions from n number of trees.
Used for both classification and regression.
It handles higher dimensionality data and missing values well and maintains accuracy, but
doesnt give precise values for the regression model as the final prediction is based on the
mean predictions from subset trees.
16. Gradient boosted trees
Boosting
Boosting refers to a family of learners which convert weak learners to strong learners.
It learns sequentially from the errors from a prior random sample(in our case, a tree).
The weak learners are trained sequentially each trying to correct its predecessor.
The early learners fit simple models to the data and then analyze the data for errors.
All the weak learners with their higher accuracy of error (only slighty less than
guessing,0.5) are combined in some way to get a strong classifier,with a higher accuracy.
When an input is misclassified by a hypothesis, its weight is increased so that next
hypothesis is more likely to classify it correctly.
By combining the whole set at the end, the weak learners are converted into better
performing model.
17. Gradient boosted trees
Types of boosting AdaBoost: short for
Adaptive boosting.
Start from a weak
classifier and learn to
linearly combine them so
that the error is reduced.
The result is strong
classifier built by
boosting of weak
classifiers.
We train an algorithm,
say Decision tree on a
model, whose all features
have been given equal
weights.
A model is built on a
subset of data and
predictions are made on
the whole dataset,and
errors are calculated by
the predictions and
actual values.
18. Gradient boosted trees
Adaboost
While creating the next model, higher weights are given to the data points which were
predicted incorrectly ie. misclassified.
Weights can be determined using the error value, ie. Higher the error, more is the weight
associated to the observation.
This process is repeated until the error function does not change, or the maximum limit of
the estimators is reached.
Its used for both classfication and regression problem,mostly decision stamps are used with
Adaboost, but any machine learning algorithm, if it accepts weight on training data set can
be used a base learner.
One of the applications of Adaboost is face recognition systems.
19. Gradient boosted trees
Types of Boosting
Gradient Boosting
We will cover this in detail now.
There are other implementations of Gradient boosting like XGBoost and Light
GB.
20. Gradient boosted trees
Gradient Boost
It’s also a machine learning technique which produces which produces a
prediction model in the form of an ensemble of weak prediction models, typically
decision trees.
Thus, they may be referred as Gradient boosted trees.
Like other boosting methods, it builds a model in a sequential or stage-wise
fashion.
21. Gradient boosted trees
We shall now see some maths behind it.
The objective of any supervised learning algorithm is to define a loss function and minimize it.
We have mean square error defined as:
We want our loss function(MSE) in our predictions be minimum using gradient descent and updating our
predictions based on a learning rate.
22. Gradient boosted trees
We will see what is learning rate.
Learning rates are the hypermeters which controls how much we are adjusting the weights of our network with
respect to the loss gradient. The learning rate affects how quickly our model can converge to a local minima (aka.
arrive at the best accuracy).
The relationship is given by the formula: new_weight = existing_weight — learning_rate * gradient
In gradient boosted trees, we use the following learning rate:
We basically update the predictions such that the sum of our residuals is close to zero(or minimum) and the
predicted values are sufficiently close to the actual values.
Learning rates are so tuned so as to prevent the overfitting which the gradient boosted trees are prone to.
23. Gradient boosted trees
In Gradient boosted trees, models are sequentially trained, and each model minimizes the
loss function (y = ax + b + e, e needs special attention as it is an error term) of the whole
system using Gradient descent method, as explained earlier.
The learning procedure consecutively fits new models to provide a more accurate estimate
of response variable.
The principle idea behind this algorithm is to create new base learners, which can be
maximally corelated with negative gradient of the loss function, associated with the whole
ensemble.
Pros of Gradient boosted trees: Fast, easy to tune, not sensitive to scale (features can be a
mix of continuous and categorical data), good performance, lots of software available(well
supported and tested)
Cons: Sensitive to overfitting and noise (should always cross validate)