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
Why Evaluating Models Matters
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- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, Jonathan Taylor, 2013 (Springer) - A foundational textbook on statistical learning, offering rigorous theoretical insights into model assessment, generalization error, and model selection, fundamental for understanding why evaluation is essential.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - A popular practical guide that covers the complete machine learning workflow, including model evaluation, cross-validation, and performance metrics, demonstrating their application for reliable model development.
- Applied Predictive Modeling, Max Kuhn and Kjell Johnson, 2013 (Springer) DOI: 10.1007/978-1-4614-6849-3 - A comprehensive book dedicated to the process of building and evaluating predictive models, covering data splitting strategies, resampling methods, and various performance metrics to ensure reliable model assessment.