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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

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

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

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

Chapter 5: Basic Evaluation Workflow

Steps in a Standard Evaluation

Choosing Metrics for Your Problem

Performing the Train-Test Split

Training a Simple Model (Conceptual)

Generating Predictions on the Test Set

Calculating Performance Metrics

Interpreting the Results

Simple Evaluation Workflow Example

Common Mistakes in Basic Evaluation

Fundamentals of Model Evaluation and Metrics

Prerequisites: No prior knowledge required

Level: Beginner

What You'll Learn

Model Evaluation Purpose
Understand why evaluating machine learning models is essential for development.
Classification Metrics
Identify and calculate fundamental metrics like accuracy, precision, recall, and F1-score.
Regression Metrics
Identify and calculate fundamental metrics like MAE, MSE, RMSE, and R-squared.
Confusion Matrix
Interpret a confusion matrix to understand classification performance.
Data Splitting
Learn the importance of train/test splits for reliable model evaluation.
Basic Evaluation Workflow
Apply a simple workflow to evaluate a model using appropriate metrics.

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