Fundamentals of Model Evaluation and Metrics
Chapter 1: Introduction to Model Evaluation
What is a Machine Learning Model?
Why Evaluating Models Matters
The Goal of Evaluation Metrics
Types of Learning Problems: Classification
Types of Learning Problems: Regression
Overview of the Evaluation Process
Quiz for Chapter 1
Chapter 2: Metrics for Classification
Understanding Classification Predictions
Accuracy: A Simple First Metric
When Accuracy Can Be Misleading
True Positives, False Positives, True Negatives, False Negatives
The Confusion Matrix Explained
Precision: Measuring Exactness
Recall (Sensitivity): Measuring Completeness
Precision vs. Recall Trade-off
F1-Score: Combining Precision and Recall
Practice: Calculating Classification Metrics
Quiz for Chapter 2
Chapter 3: Metrics for Regression
Understanding Regression Predictions
Calculating Prediction Errors
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
Comparing MAE, MSE, and RMSE
Coefficient of Determination (R-squared)
Interpreting R-squared Values
Limitations of R-squared
Practice: Calculating Regression Metrics
Quiz for Chapter 3
Chapter 4: Preparing Data for Evaluation
Why Evaluate on Unseen Data?
The Training Set: Learning Patterns
The Test Set: Assessing Performance
Train-Test Split Procedure
Common Split Ratios
Randomness in Splitting
Potential Issues with a Single Split
Introduction to Cross-Validation Concept
Hands-on Practical: Splitting Data
Quiz for Chapter 4
Chapter 5: Basic Evaluation Workflow
Steps in a Standard Evaluation
Choosing Metrics for Your Problem
Performing the Train-Test Split
Training a Simple Model
Generating Predictions on the Test Set
Calculating Performance Metrics
Interpreting the Results
Simple Evaluation Workflow Example
Common Mistakes in Basic Evaluation
Quiz for Chapter 5
Potential Issues with a Single Split
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- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - This book covers statistical learning methods, including model assessment, selection, and the bias-variance trade-off, which explains the issues with a single split and the utility of cross-validation.
- A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, Ron Kohavi, 1995 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI), Vol. 14 (International Joint Conference on Artificial Intelligence (IJCAI)) DOI: 10.5555/164319.164334 - This paper compares cross-validation and bootstrap methods for estimating model accuracy, providing evidence for their use in reliable performance estimation.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - This guide explains robust model evaluation, covering the limitations of a single train-test split and the advantages of cross-validation for more dependable performance estimates.
- Cross-validation: evaluating estimator performance, Scikit-learn developers, 2023 - This official documentation explains cross-validation, its purpose, and why it is preferred over a single train-test split for obtaining a more stable performance estimate.