This document discusses interpreting machine learning models and summarizes techniques for interpreting random forests. Random forests are considered "black boxes" due to their complexity but their predictions can be explained by decomposing them into mathematically exact feature contributions. Decision trees can also be interpreted by defining the prediction as a bias plus the contributions from each feature along the decision path. This operational view of decision trees can be extended to interpret random forest predictions despite their complexity.
H2O.ai's Distributed Deep Learning by Arno Candel 04/03/14Sri Ambati
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
http://docs.0xdata.com/datascience/deeplearning.html
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
A Random Forest Approach To Skin Detection With RAuro Tripathy
The document discusses using random forests for skin detection from images. It provides an overview of the scheme, which uses improved Hue, Saturation, Luminance (IHLS) color space and transforms RGB values to IHLS features. Random forests showed the best performance compared to other models. The document references code and datasets, provides details on implementing random forests in R, and states results were incomplete due to a small training set and need for parallel computing. It concludes with time for questions.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Jan vitek distributedrandomforest_5-2-2013Sri Ambati
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Valencian Summer School 2015
Day 1
Lecture 3
Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
This summary covers the key topics discussed in a morning machine learning class:
- The class covered the state of machine learning including common problems, tasks, features, and technology used. Decision trees and ensembles of decision trees were explained in detail.
- Data transformations and feature engineering techniques were also discussed including discretization, normalization, projections, and handling imbalanced datasets. Evaluating machine learning algorithms and dealing with imbalanced data were additional topics.
This presentation discusses random forest modeling using the Mahout machine learning library. It describes random forests and the Mahout library. The presentation then demonstrates how to generate a random forest model in Mahout on Hadoop, including data preparation, model training, testing the model, and tuning hyperparameters. It also discusses techniques like selecting random subsets of features to split on and using the Gini index to evaluate splits.
H2O.ai's Distributed Deep Learning by Arno Candel 04/03/14Sri Ambati
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.
http://docs.0xdata.com/datascience/deeplearning.html
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
A Random Forest Approach To Skin Detection With RAuro Tripathy
The document discusses using random forests for skin detection from images. It provides an overview of the scheme, which uses improved Hue, Saturation, Luminance (IHLS) color space and transforms RGB values to IHLS features. Random forests showed the best performance compared to other models. The document references code and datasets, provides details on implementing random forests in R, and states results were incomplete due to a small training set and need for parallel computing. It concludes with time for questions.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Jan vitek distributedrandomforest_5-2-2013Sri Ambati
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Valencian Summer School 2015
Day 1
Lecture 3
Decision Trees
Gonzalo Martínez (UAM)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
This summary covers the key topics discussed in a morning machine learning class:
- The class covered the state of machine learning including common problems, tasks, features, and technology used. Decision trees and ensembles of decision trees were explained in detail.
- Data transformations and feature engineering techniques were also discussed including discretization, normalization, projections, and handling imbalanced datasets. Evaluating machine learning algorithms and dealing with imbalanced data were additional topics.
This presentation discusses random forest modeling using the Mahout machine learning library. It describes random forests and the Mahout library. The presentation then demonstrates how to generate a random forest model in Mahout on Hadoop, including data preparation, model training, testing the model, and tuning hyperparameters. It also discusses techniques like selecting random subsets of features to split on and using the Gini index to evaluate splits.
This document discusses techniques for preprocessing data including data cleaning, transformation, integration, and reduction. Data cleaning removes noise and inconsistencies by filling missing values and identifying outliers. Data integration merges data from multiple sources while dealing with issues like different naming conventions. Data transformation techniques include normalization, aggregation, generalization, attribute selection, and dimensionality reduction to reduce data size and handle inconsistencies. Preprocessing is needed to clean noisy, inconsistent data and handle incomplete data from various sources.
Data preprocessing involves cleaning data by filling in missing values, smoothing noisy data, and resolving inconsistencies. It also includes integrating and transforming data from multiple sources, reducing data volume through aggregation, dimensionality reduction, and discretization while maintaining analytical results. The key goals of preprocessing are to improve data quality and prepare the data for mining tasks through techniques like data cleaning, integration, transformation, reduction, and discretization of attributes into intervals or concept hierarchies.
This document discusses a project to evaluate and visualize different data mining techniques. The purpose is to implement data mining algorithms, visualize the results, and compare algorithm performance on datasets. It will handle different data types, perform preprocessing, implement clustering algorithms like K-Means and hierarchical clustering, visualize models, and compare algorithms based on metrics like runtime. It provides an overview of K-Means and hierarchical single-linkage clustering, explaining their processes at a high level.
This document provides an overview of machine learning concepts including:
1) The main types of machine learning problems are classification, regression, clustering, and optimization.
2) Supervised, unsupervised, and reinforcement learning are the main approaches to teach a machine.
3) Building machine learning models involves data collection/preparation, model training/testing, and deployment of the trained model.
Data mining involves analyzing large datasets to extract hidden patterns and predictive information. It is used to discover useful information from large data repositories like data warehouses. The document discusses data mining concepts like data extraction, data warehousing, the data mining process, applications, and issues. Major trends in data mining include datafication of enterprises, use of Hadoop for large datasets, and in-database analytics for performance.
This document discusses data preprocessing techniques for machine learning. It covers common preprocessing steps like normalization, encoding categorical features, and handling outliers. Normalization techniques like StandardScaler, MinMaxScaler and RobustScaler are described. Label encoding and one-hot encoding are covered for processing categorical variables. The document also discusses polynomial features, custom transformations, and preprocessing text and image data. The goal of preprocessing is to prepare data so it can be better consumed by machine learning algorithms.
This document discusses data preprocessing techniques. It defines data preprocessing as transforming raw data into an understandable format. Major tasks in data preprocessing are described as data cleaning, integration, transformation, and reduction. Data cleaning involves handling missing data, noisy data, and inconsistencies. Data integration combines data from multiple sources. Data transformation techniques include smoothing, aggregation, generalization, and normalization. The goal of data reduction is to reduce the volume of data while maintaining analytical results.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
Data mining technique for classification and feature evaluation using stream ...ranjit banshpal
This document discusses data stream mining techniques for classification and feature evaluation. It introduces data stream mining and its applications, including network traffic analysis and sensor data. It describes decision trees and the VFDT algorithm for data stream classification. VFDT can classify high-dimensional data streams more efficiently than decision trees. The document also covers challenges in data stream mining like concept drift and feature evolution, and concludes by discussing applications and referencing related work.
This document discusses XGBoost, an optimized distributed gradient boosting library. It begins by explaining what XGBoost can do, including binary classification, multiclass classification, regression, and learning to rank. It then discusses boosted trees and their variants like GBDT, GBRT, and MART. It explains how tree ensembles work by combining many decision trees to make predictions and describes XGBoost's additive training process of greedily adding trees to minimize loss. It also covers XGBoost's efficient splitting algorithm for growing trees and references for further information.
Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques include data cleaning, integration, transformation, reduction, discretization, and generating concept hierarchies.
This document discusses data preprocessing techniques. It describes how real-world data can be incomplete, noisy, or inconsistent. The main tasks in data preprocessing are cleaning the data by handling missing values and outliers, integrating and transforming the data by normalization and aggregation, reducing the data through dimensionality reduction, and discretizing numeric values. Specific techniques discussed include binning, clustering, regression, principle component analysis, and information-based discretization. The goal is to prepare raw data for further analysis.
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
The document provides an introduction to the Orange data mining and visualization tool. It discusses what data mining is and its major tasks, including classification, clustering, deviation detection, forecasting, and description. It also lists major industries that use data mining, such as retail, finance, education, and healthcare. The document then introduces Orange, describing it as an open-source, component-based, visual programming software that allows data mining through visual programming and Python scripting without requiring any programming. It provides a link to download Orange and walks through loading a heart disease dataset and exploring it using various algorithms like KNN, Naive Bayes, decision trees, random forests, logistic regression, and neural networks. Performance results are compared for different algorithms
The document discusses various techniques for data pre-processing. It begins by explaining why pre-processing is important for obtaining clean and consistent data needed for quality data mining results. It then covers topics such as data cleaning, integration, transformation, reduction, and discretization. Data cleaning involves techniques for handling missing values, outliers, and inconsistencies. Data integration combines data from multiple sources. Transformation techniques include normalization, aggregation, and generalization. Data reduction aims to reduce data volume while maintaining analytical quality. Discretization includes binning of continuous variables.
Data preprocessing involves cleaning data by handling missing values, noisy data, and inconsistencies. It also includes data reduction techniques like discretization which reduce data volume while maintaining analytical results. The goals of preprocessing are to improve data quality, handle problems like incomplete, noisy, and inconsistent data for effective data mining and analysis.
The document discusses various data reduction strategies including attribute subset selection, numerosity reduction, and dimensionality reduction. Attribute subset selection aims to select a minimal set of important attributes. Numerosity reduction techniques like regression, log-linear models, histograms, clustering, and sampling can reduce data volume by finding alternative representations like model parameters or cluster centroids. Dimensionality reduction techniques include discrete wavelet transformation and principal component analysis, which transform high-dimensional data into a lower-dimensional representation.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
This document contains legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that performance depends on system configuration and that sample source code is released under an Intel license agreement. Finally, it provides basic copyright information.
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
This document discusses techniques for preprocessing data including data cleaning, transformation, integration, and reduction. Data cleaning removes noise and inconsistencies by filling missing values and identifying outliers. Data integration merges data from multiple sources while dealing with issues like different naming conventions. Data transformation techniques include normalization, aggregation, generalization, attribute selection, and dimensionality reduction to reduce data size and handle inconsistencies. Preprocessing is needed to clean noisy, inconsistent data and handle incomplete data from various sources.
Data preprocessing involves cleaning data by filling in missing values, smoothing noisy data, and resolving inconsistencies. It also includes integrating and transforming data from multiple sources, reducing data volume through aggregation, dimensionality reduction, and discretization while maintaining analytical results. The key goals of preprocessing are to improve data quality and prepare the data for mining tasks through techniques like data cleaning, integration, transformation, reduction, and discretization of attributes into intervals or concept hierarchies.
This document discusses a project to evaluate and visualize different data mining techniques. The purpose is to implement data mining algorithms, visualize the results, and compare algorithm performance on datasets. It will handle different data types, perform preprocessing, implement clustering algorithms like K-Means and hierarchical clustering, visualize models, and compare algorithms based on metrics like runtime. It provides an overview of K-Means and hierarchical single-linkage clustering, explaining their processes at a high level.
This document provides an overview of machine learning concepts including:
1) The main types of machine learning problems are classification, regression, clustering, and optimization.
2) Supervised, unsupervised, and reinforcement learning are the main approaches to teach a machine.
3) Building machine learning models involves data collection/preparation, model training/testing, and deployment of the trained model.
Data mining involves analyzing large datasets to extract hidden patterns and predictive information. It is used to discover useful information from large data repositories like data warehouses. The document discusses data mining concepts like data extraction, data warehousing, the data mining process, applications, and issues. Major trends in data mining include datafication of enterprises, use of Hadoop for large datasets, and in-database analytics for performance.
This document discusses data preprocessing techniques for machine learning. It covers common preprocessing steps like normalization, encoding categorical features, and handling outliers. Normalization techniques like StandardScaler, MinMaxScaler and RobustScaler are described. Label encoding and one-hot encoding are covered for processing categorical variables. The document also discusses polynomial features, custom transformations, and preprocessing text and image data. The goal of preprocessing is to prepare data so it can be better consumed by machine learning algorithms.
This document discusses data preprocessing techniques. It defines data preprocessing as transforming raw data into an understandable format. Major tasks in data preprocessing are described as data cleaning, integration, transformation, and reduction. Data cleaning involves handling missing data, noisy data, and inconsistencies. Data integration combines data from multiple sources. Data transformation techniques include smoothing, aggregation, generalization, and normalization. The goal of data reduction is to reduce the volume of data while maintaining analytical results.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
The document introduces data preprocessing techniques for data mining. It discusses why data preprocessing is important due to real-world data often being dirty, incomplete, noisy, inconsistent or duplicate. It then describes common data types and quality issues like missing values, noise, outliers and duplicates. The major tasks of data preprocessing are outlined as data cleaning, integration, transformation and reduction. Specific techniques for handling missing values, noise, outliers and duplicates are also summarized.
Data mining technique for classification and feature evaluation using stream ...ranjit banshpal
This document discusses data stream mining techniques for classification and feature evaluation. It introduces data stream mining and its applications, including network traffic analysis and sensor data. It describes decision trees and the VFDT algorithm for data stream classification. VFDT can classify high-dimensional data streams more efficiently than decision trees. The document also covers challenges in data stream mining like concept drift and feature evolution, and concludes by discussing applications and referencing related work.
This document discusses XGBoost, an optimized distributed gradient boosting library. It begins by explaining what XGBoost can do, including binary classification, multiclass classification, regression, and learning to rank. It then discusses boosted trees and their variants like GBDT, GBRT, and MART. It explains how tree ensembles work by combining many decision trees to make predictions and describes XGBoost's additive training process of greedily adding trees to minimize loss. It also covers XGBoost's efficient splitting algorithm for growing trees and references for further information.
Data preprocessing involves cleaning data by handling missing values, noise, and inconsistencies. It also includes integrating and transforming data through normalization, aggregation, and dimensionality reduction. The goals are to improve data quality and reduce data volume for mining while maintaining the essential information. Techniques include data cleaning, integration, transformation, reduction, discretization, and generating concept hierarchies.
This document discusses data preprocessing techniques. It describes how real-world data can be incomplete, noisy, or inconsistent. The main tasks in data preprocessing are cleaning the data by handling missing values and outliers, integrating and transforming the data by normalization and aggregation, reducing the data through dimensionality reduction, and discretizing numeric values. Specific techniques discussed include binning, clustering, regression, principle component analysis, and information-based discretization. The goal is to prepare raw data for further analysis.
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
The document provides an introduction to the Orange data mining and visualization tool. It discusses what data mining is and its major tasks, including classification, clustering, deviation detection, forecasting, and description. It also lists major industries that use data mining, such as retail, finance, education, and healthcare. The document then introduces Orange, describing it as an open-source, component-based, visual programming software that allows data mining through visual programming and Python scripting without requiring any programming. It provides a link to download Orange and walks through loading a heart disease dataset and exploring it using various algorithms like KNN, Naive Bayes, decision trees, random forests, logistic regression, and neural networks. Performance results are compared for different algorithms
The document discusses various techniques for data pre-processing. It begins by explaining why pre-processing is important for obtaining clean and consistent data needed for quality data mining results. It then covers topics such as data cleaning, integration, transformation, reduction, and discretization. Data cleaning involves techniques for handling missing values, outliers, and inconsistencies. Data integration combines data from multiple sources. Transformation techniques include normalization, aggregation, and generalization. Data reduction aims to reduce data volume while maintaining analytical quality. Discretization includes binning of continuous variables.
Data preprocessing involves cleaning data by handling missing values, noisy data, and inconsistencies. It also includes data reduction techniques like discretization which reduce data volume while maintaining analytical results. The goals of preprocessing are to improve data quality, handle problems like incomplete, noisy, and inconsistent data for effective data mining and analysis.
The document discusses various data reduction strategies including attribute subset selection, numerosity reduction, and dimensionality reduction. Attribute subset selection aims to select a minimal set of important attributes. Numerosity reduction techniques like regression, log-linear models, histograms, clustering, and sampling can reduce data volume by finding alternative representations like model parameters or cluster centroids. Dimensionality reduction techniques include discrete wavelet transformation and principal component analysis, which transform high-dimensional data into a lower-dimensional representation.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
This document contains legal notices and disclaimers for an Intel presentation. It states that the presentation is for informational purposes only and that Intel makes no warranties. It also notes that performance depends on system configuration and that sample source code is released under an Intel license agreement. Finally, it provides basic copyright information.
One of the most important, yet often overlooked, aspects of predictive modeling is the transformation of data to create model inputs, better known as feature engineering (FE). This talk will go into the theoretical background behind FE, showing how it leverages existing data to produce better modeling results. It will then detail some important FE techniques that should be in every data scientist’s tool kit.
Random forest is an ensemble learning technique that builds multiple decision trees and merges their predictions to improve accuracy. It works by constructing many decision trees during training, then outputting the class that is the mode of the classes of the individual trees. Random forest can handle both classification and regression problems. It performs well even with large, complex datasets and prevents overfitting. Some key advantages are that it is accurate, efficient even with large datasets, and handles missing data well.
The Machine Learning Workflow with AzureIvo Andreev
This document provides an overview of real world machine learning using Azure. It discusses the machine learning workflow including data understanding, preprocessing, feature engineering, model selection, evaluation and tuning. It then describes various Azure machine learning tools for building, testing and deploying machine learning models including Azure ML Workbench, Studio, Experimentation Service and Model Management Service. It concludes with an upcoming demo of predictive maintenance using Azure ML Studio.
AWS re:Invent 2016: Getting to Ground Truth with Amazon Mechanical Turk (MAC201)Amazon Web Services
Jump-start your machine learning project by using the crowd to build your training set. Before you can train your machine learning algorithm, you need to take your raw inputs and label, annotate, or tag them to build your ground truth. Learn how to use the Amazon Mechanical Turk marketplace to perform these tasks. We share Amazon's best practices, developed while training our own machine learning algorithms, and walk you through quickly getting affordable and high-quality training data.
Foundations of Machine Learning - StampedeCon AI Summit 2017StampedeCon
This presentation will cover all aspects of modeling, from preparing data, training and evaluating the results. There will be descriptions of the mainline ML methods including, neural nets, SVM, boosting, bagging, trees, forests, and deep learning. common problems of overfitting and dimensionality will be covered with discussion of modeling best practices. Other topics will include field standardization, encoding categorical variables, feature creation and selection. It will be a soup-to-nuts overview of all the necessary procedures for building state-of-the art predictive models.
This document discusses feature engineering and machine learning approaches for predicting customer behavior. It begins with an overview of feature engineering, including how it is used for image recognition, text mining, and generating new variables from existing data. The document then discusses challenges with artificial intelligence and machine learning models, particularly around explainability. It concludes that for smaller datasets, feature engineering can improve predictive performance more than complex machine learning models, while large datasets are better suited to machine learning approaches. Testing on a small travel acquisition dataset confirmed that traditional models with feature engineering outperformed neural networks.
This document provides an overview of exploratory data analysis (EDA). It discusses the key goals of EDA as understanding the characteristics of a dataset and selecting appropriate analysis tools. The document outlines common EDA tasks like calculating summary statistics, creating visualizations, and detecting patterns and anomalies. Specific techniques covered include frequency tables, measures of central tendency and spread, histograms, box plots, contingency tables, and scatter plots. The document emphasizes exploring one variable at a time before examining relationships between multiple variables to better understand the dataset.
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
Lecture 2 - Introduction to Machine Learning, a lecture in subject module Sta...Maninda Edirisooriya
Introduction to Statistical and Machine Learning. Explains basics of ML, fundamental concepts of ML, Statistical Learning and Deep Learning. Recommends the learning sources and techniques of Machine Learning. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
PCA is a dimensionality reduction technique that transforms a number of potentially correlated variables into a smaller number of uncorrelated variables called principal components. It works by geometrically projecting the data onto a lower dimensional space in a way that retains as much of the variation present in the original data as possible. PCA is widely used for applications like data visualization, feature extraction and dimensionality reduction. An example of using PCA in genetics is shown, where the variability in human DNA data can be strongly linked to geographical patterns through principal component analysis.
Introduction to Datamining Concept and TechniquesSơn Còm Nhom
This document provides an introduction to data mining techniques. It discusses data mining concepts like data preprocessing, analysis, and visualization. For data preprocessing, it describes techniques like similarity measures, down sampling, and dimension reduction. For data analysis, it explains clustering, classification, and regression methods. Specifically, it gives examples of k-means clustering and support vector machine classification. The goal of data mining is to retrieve hidden knowledge and rules from data.
An introduction to machine learning and statisticsSpotle.ai
This document provides an overview of machine learning and predictive modeling. It begins by describing how predictive models can be used in various domains like healthcare, finance, telecom, and business. It then discusses the differences between machine learning and predictive modeling, noting that machine learning aims to allow machines to learn autonomously using feedback mechanisms, while predictive modeling focuses on building statistical models to predict outcomes. The document also uses examples like Microsoft's Tay chatbot to illustrate how machine learning systems can be exposed to real-world data to continuously learn and improve. It concludes by explaining how predictive analytics fits within machine learning as the starting point to build initial predictive models and continuously monitor and refine them.
The document summarizes an Analytics Vidhya meetup event. It discusses that the meetups will occur once a month, with the next one on May 24th. It aims to provide networking and learning around data science, big data, machine learning and IoT. It introduces the volunteer organizers and outlines the agenda, which includes an introduction, discussing the model building lifecycle, data exploration techniques, and modeling techniques like logistic regression, decision trees, random forests, and SVMs. It provides details on practicing these techniques by predicting survival on the Titanic dataset.
Agile analytics : An exploratory study of technical complexity managementAgnirudra Sikdar
The thesis involved the reviewing of various case studies to determine the types of modelling, choice of algorithm, types of analytical approaches and trying to determine the various complexities arising from these cases. From these reviews, procedures have been proposed to improve the efficiency and manage the various types of complexities from using agile methodological perspective. Focus was mostly done on Customer Segmentation and Clustering , with the sole purpose to bridge Big Data and Business Intelligence together using Analytic.
State of the Art in Machine Learning, by Thomas Dietterich, Distinguished Professor Emeritus in the School of EECS at Oregon State University and Chief Scientist of BigML.
*MLSEV 2020: Virtual Conference.
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
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
Similar to Interpreting machine learning models (20)
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
2. About me
• Senior applied scientist at Microsoft,
• Using ML and statistics to improve call quality in Skype
• Various projects on user engagement modelling, Skype user graph analysis,
call reliability modelling, traffic shaping detection
• Blogging on data science and ML @ http://blog.datadive.net/
3. Machine learning and model interpretation
• Machine learning studies algorithms that learn from data and make
predictions
• Learning algorithms are about correlations in the data
• In contrast, in data science and data mining, understanding causality
is essential
• Applying domain knowledge requires understanding and interpreting
models
4. Usefulness of model interpretation
• Often, we need to understand individual predictions a model is
making.
For example a model may
• Recommend a treatment for a patient or estimate a disease to be
likely. The doctor needs to understand the reasoning.
• Classify a user as a scammer, but the user disputes it. The fraud
analyst needs to understand why the model made the classification.
• Predict that a video call will be graded poorly by the user. The
engineer needs to understand why this type of call was considered
problematic.
5. Usefulness of model interpretation cont.
• Understanding differences on a dataset level.
• Why is a new software release receiving poorer feedback from customers
when compared to the previous one?
• Why are grain yields in one region higher than the other?
• Debugging models. A model that worked earlier is giving unexpected
results on newer data.
6. Algorithmic transparency
• Algorithmic transparency becoming a requirement in many fields
• French Conseil d’Etat (State Council’s) recommendation in „Digital
technology and fundamental rights“(2014) : Impose to algorithm-
based decisions a transparency requirement, on personal data used
by the algorithm, and the general reasoning it followed.
• Federal Trade Commission (FTC) Chair Edith Ramirez: The agency is
concerned about ‘algorithmic transparency /../’ (Oct 2015).
FTC Office of Technology Research and Investigation started in March
2015 to tackle algorithmic transparency among other issues
7. Interpretable models
• Traditionally, two types of (mainstream) models considered when
interpretability is required
• Linear models (linear and logistic regression)
• 𝑌 = 𝑎 + 𝑏1 𝑥1 + ⋯ + 𝑏 𝑛 𝑥 𝑛
• heart_disease = 0.08*tobacco + 0.043*age + 0.939*famhist + ...
(from Elements of statistical learning)
• Decision trees Family hist
Tobacco
Age > 60
Yes No
60% 20%40% 30%
8. Example: heart disease risk prediction
• Essentially a linear model
with integer coefficients
• Easy for a doctor to follow
and explain
From National Heart, Lung and Blood
Institute.
9. Linear models have drawbacks
• Underlying data is often non-linear
• Feature binning: potentially massive
increase in number of features
• Basis expansion (non-linear transfor-
mations of underlying features)
In both cases performance will be traded for interpretability
𝑌 = 2𝑥1 + 𝑥2 − 3𝑥3
𝑌 = 2𝑥1
2 + 3𝑥1 − log 𝑥2 + 𝑥2 𝑥3 + …
vs
10. Decision trees
• Decision trees can fit to non-linear data
• Work well with both categorical and continuous data, classification
and regression
• Easy to understand
Rooms < 3
Built_year < 2008
Crime rate < 3
35,00045,000
Floor < 2
55,00030,000
70,000
52,000
Crime_rate < 5
Yes No
11. (Small part of) default decision tree in
scikit-learn. Boston housing data
500 data points
14 features
12. Decision trees
• Decision trees are understandable only when they are (very) small
• Tree of depth n has up 2n leaves and 2n -1 internal nodes. With depth 20, a
tree can have up to 1048576 leaves
• Previous slide had <200 nodes
• Additionally decision trees are high variance method – low
generalization, tend to overfit
13. Random forests
• Can learn non-linear relationships in the data well
• Robust to outliers
• Can deal with both continuous and categorical data
• Require very little input preparation (see previous three points)
• Fast to train and test, trivially parallelizable
• High accuracy even with minimal meta-optimization
• Considered to be a black box that is difficult or impossible to interpret
14. Random forests as a black box
• Consist of a large number of decision trees (often 100s to 1000s)
• Trained on bootstrapped data (sampling with replacement)
• Using random feature selection
15. Random forests as a black box
• "Black box models such as random forests can't quantify the impact of each
predictor to the predictions of the complex model“, in PRICAI 2014: Trends
in Artificial Intelligence
• Unfortunately, the random forest algorithm is a black box when it comes to
evaluating the impact of a single feature on the overall performance. In
Advances in Natural Language Processing 2014
• “(Random forest model) is close to a black box in the sense that it uses 810
features /../ reduction in the number of features would allow an operator
to study individual decisions to have a rough idea how the global decision
could have been made”. In Advances in Data Mining: Applications and
Theoretical Aspects: 2014
16. Understanding the model vs the predictions
• Keep in mind, we want to understand why a particular decision was
made. Not necessarily every detail of the full model
• As an analogy, we don’t need to understand how a brain works to
understand why a person made a particular a decision: simple
explanation can be sufficient
• Ultimately, as ML models get more complex and powerful, hoping to
understand the models themselves is doomed to failure
• We should strive to make models explain their decisions
17. Turning the black box into a white box
• In fact, random forest predictions can be explained and interpreted,
by decomposing predictions into mathematically exact feature
contributions
• Independently of the
• number of features
• number of trees
• depth of the trees
18. • Classical definition (from Elements of statistical learning)
• Tree divides the feature space into M regions Rm (one for each leaf)
• Prediction for feature vector x is the constant cm associated with region Rm the
vector x belongs to
𝑑𝑡 𝑥 =
𝑚=1
𝑀
𝑐 𝑚 𝐼(𝑥 ∈ 𝑅 𝑚)
Revisiting decision trees
19. Example decision tree – predicting apartment
prices
Rooms < 3
Crime rate < 3
35,00045,000
Floor < 2
55,00030,000
70,000
52,000
Crime_rate < 5
Yes No
Built_year > 2008
20. Estimating apartment prices
Assume an apartment [2 rooms; Built in 2010; Neighborhood crime rate: 5]
We walk the tree to obtain the price
Rooms < 3
Floor < 2
55,00030,000
70,000
52,000
Crime_rate < 5
Yes No
Crime rate < 3
35,00045,000
Built_year > 2008
24. Operational view
• Classical definition ignores the operational aspect of the tree.
• There is a decision path through the tree
• All nodes (not just the leaves) have a value associated with them
• Each decision along the path contributes something to the final
outcome
• A feature is associated with every decision
• We can compute the final outcome in terms of feature contributions
31. • We can define the the decision tree in term of the bias and contribution
from each feature.
• Similar to linear regression (prediction = bias + feature1contribution + … +
featurencontribution), but on a prediction level, not model level
• Does not depend on the size of the tree or number of features
• Works equally well for
• Regression and classification trees
• Multivalued and multilabel data
Decomposition for decision trees
dt(𝑥) = 𝑏𝑖𝑎𝑠 + 𝑖=1
𝑁
𝑐𝑜𝑛𝑡𝑟(𝑖, 𝑥))dt 𝑥 = 𝑚=1
𝑀
𝑐 𝑚 𝐼(𝑥 ∈ 𝑅 𝑚)
32. Deeper inspections
• We can have more fine grained definition in addition to pure feature
contributions
• Separate negative and positive contributions
• Contribution from decisions (floor = 1 -15000)
• Contribution from interactions (floor == 1 & has_terrace 3000)
• etc
• Number of features typically not a concern because of the long tail.
In practice, top 10 features contribute the vast majority of the overall
deviation from the mean
33. From decision trees to random forests
• Prediction of a random forest is the
average of the predictions of individual
trees
• From distributivity of multiplication and
associativity of addition, random forest prediction can be defined as
the average of bias term of individual trees + sum of averages of each
feature contribution from individual trees
• prediction = bias + feature1contribution + … + featurencontribution
RF 𝑥 =
1
𝐽
𝑗=1
𝐽
𝑑𝑡𝑗(𝑥)
RF 𝑥 =
1
𝐽
𝑗=1
𝐽
𝑏𝑖𝑎𝑠𝑗 𝑥 + (
1
𝐽
𝑗=1
𝐽
𝑐𝑜𝑛𝑡𝑟𝑗 1, 𝑥 + ⋯ +
1
𝐽
𝑗=1
𝐽
𝑐𝑜𝑛𝑡𝑟𝑗 𝑛, 𝑥 )
34. Random forest interpretation in Scikit-Learn
• Path walking in general no supported by ML libraries
• Scikit-Learn: one of the most popular Python (and overall) ML
libraries
• Patch since 0.17 (released Nov 8 2015) to include values/predictions
at each node: allows walking the tree paths and collecting values
along the way
• Treeintepreter library for decomposing the predictions
• https://github.com/andosa/treeinterpreter
• pip install treeinterpreter
• Supports both decision tree and random forest classes, regression
and classification
35. Using treeinterpreter
• Decomposing predictions is a one-liner
from treeinterpreter import treeinterpreter as ti
rf = RandomForestRegressor()
rf.fit(trainX,trainY)
prediction, bias, contributions = ti.predict(rf, testX)
#prediction == bias + contributions
assert(numpy.allclose(prediction, bias + np.sum(contributions, axis=1)))
37. Conclusions
• Model interpretation can be very beneficial for ML and data science
practitioners in many tasks
• No need to understand the full model in many/most cases: explanation of
decisions sufficient
• Random forests can be turned from black box into a white box
• Each prediction can decomposed into bias and feature contribution terms
• Python library available for scikit-learn at
https://github.com/andosa/treeinterpreter
or pip install treeinterpreter
• Subscribe to http://blog.datadive.net/
• Follow @crossentropy