Module 4: Bias-Variance Tradeoff

Overview

In this module, you will learn about the bias-variance tradeoff, a fundamental concept in machine learning that helps us understand model performance and generalization. You'll explore how to balance model complexity to avoid both underfitting and overfitting, and you'll learn about metrics for evaluating model performance.

Learning Objectives

Objective 1: Describe the use of different metrics for evaluating model performance

Learn how to use various metrics to evaluate and compare regression model performance.

  • Understanding Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)
  • Using R-squared and adjusted R-squared for model evaluation
  • Implementing cross-validation for robust model assessment
  • Selecting appropriate metrics for different modeling scenarios

Objective 2: Understand and define bias and variance

Learn the core concepts of bias and variance and how they affect model performance.

  • Understanding the concept of bias in machine learning models
  • Understanding the concept of variance in machine learning models
  • Recognizing model complexity's effect on bias and variance
  • Identifying underfitting and overfitting in models

Objective 3: Describe the bias/variance tradeoff

Learn how to balance the competing concerns of bias and variance to build optimal models.

  • Understanding the fundamental tradeoff between bias and variance
  • Using train/test splits to evaluate model generalization
  • Implementing regularization techniques to manage the tradeoff
  • Making informed decisions about model complexity

Guided Project

Bias-Variance Tradeoff

Resources:

GitHub: Bias Variance

The notebook for this guided project is bias_variance_prop_lesson_DS39.ipynb in the GitHub repository.

Module Assignment

Bias-Variance Metrics Assignment

In this module assignment, found in the file DS_134_Metrics_Bias_Assignment_AG.ipynb in the GitHub repository, you'll apply your knowledge of the bias-variance tradeoff to evaluate and improve model performance:

Tasks:

  1. Interpret your model results using OLS and Sklearn metrics
  2. Define and analyze bias in your model
  3. Define and analyze variance in your model

Additional Resources

Documentation and Tutorials

Scikit-learn: Metrics and Scoring Scikit-learn: Cross-validation Scikit-learn: Ridge Regression

Articles and Readings

Understanding the Bias-Variance Tradeoff A Gentle Introduction to the Bias-Variance Trade-Off