Evaluating Classification and Regression Systems, Part 2



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Video description

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4 Hours of Video Instruction

Description

Code-along sessions move you from introductory machine learning concepts to concrete code.

Overview

Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond following along in discussions to coding machine learning tasks. These videos show you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends.

You learn about the fundamental metrics used to evaluate general learning systems and specific metrics used in classification and regression. You will learn techniques for getting the most informative learning performance measures out of your data. You will come away with a strong toolbox of numerical and graphical techniques to understand how your learning system will perform on novel data.

About the Instructor

Mark Fenner, PhD, has been teaching computing and mathematics to diverse adult audiences since 1999. His research projects have addressed design, implementation, and performance of machine learning and numerical algorithms, learning systems for security analysis of software repositories and intrusion detection, probabilistic models of protein function, and analysis and visualization of ecological and microscopy data. Mark continues to work across the data science spectrum from C, Fortran, and Python implementation to statistical analysis and visualization. He has delivered training and developed curriculum for Fortune 50 companies, boutique consultancies, and national-level research laboratories. Mark holds a Ph.D. in Computer Science and owns Fenner Training and Consulting, LLC.

Skill Level

Beginner to Intermediate

Learn How To

  1. Recognize underfitting and overfitting with graphical plots.
  2. Make use of resampling techniques like cross-validation to get the most out of your data.
  3. Graphically evaluate the learning performance of learning systems
  4. Compare production learners with baseline models over various classification metrics
  5. Build and evaluate confusion matrices and ROC curves
  6. Apply classification metrics to multi-class learning problems
  7. Develop precision-recall and lift curves for classifiers
  8. Compare production regression techniques with baseline regressors over various regression metrics
  9. Construct residual plots for regressors

Who Should Take This Course

Read more course:  Deep Learning with Python

This course is a good fit for anyone that needs to improve their fundamental understanding of machine learning concepts and become familiar with basic machine learning code. You might be a newer data scientist, a data analyst transitioning to the use of machine learning models, a research and development scientist looking to add machine learning techniques to your classical statistical training, or a manager adding data science/machine learning capabilities to your team.

Course Requirements

Students should have a basic understanding of programming in Python (variables, basic control flow, simple scripts). They should also have familiarity with the vocabulary of machine learning (dataset, training set, test set, model), but knowledge about the concepts can be very shallow. They should have a working Python installation that allows you to use scikit-learn and matplotlib.

Lesson Descriptions

Lesson 1: Evaluating Learning Performance

Lesson 1 covers fundamental issues with learning systems and techniques to assess them. In Lesson 1, you learn about overfitting and underfitting: these happen when our model, data, and noise in the system interact with each other poorly. To identify these scenarios, we need to make clever use, and even reuse, of our data. We also look at general techniques to graphically view the performance of our model(s) and how they interact with the data.

Lesson 2: Evaluating Classifiers (Part 1)

Lessons 2 and 3 are about specific issues in evaluating classification systems. In Lesson 2, we focus on the confusion matrix and metrics derived from it. The confusion matrix lays out the ways we are right and the ways we are wrong on an outcome-by-outcome basis. Here we focus on the case where we have two outcomes of interest.

Lesson 3: Evaluating Classifiers (Part 2)

In Lesson 3, we extend the discussion to include cases where we have more than two outcomes of interest. We discuss several approaches to multi-class evaluation and also talk about some classification specific graphical techniques: cumulative response and lift curves. We finish the lesson with a case study comparison of classifiers.

Lesson 4: Evaluating Regressors

Lesson 4 discusses techniques specific to evaluating regressors. We talk about various regression metrics and also about how to develop custom, user-defined metrics. We look at graphical evaluation techniques and take a quick look at pipelines and standardization. We conclude with a case study comparing several different regression systems.

 

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