All Courses

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

Quiz
Beta

Chapter: Metrics for Regression

Test your understanding and practice the concepts from this chapter

Quiz Instructions

This quiz contains 18 questions to help you practice.
You need to score at least 70% to pass.
Attempts: Unlimited.
Your highest score will be kept.
Please attempt this quiz without assistance; however, feel free to refer to the chapter notes or use a code interpreter if needed.
This assessment is for your learning and understanding, It is not formally accredited (yet).

Question Format

The questions are designed to be engaging, focusing on understanding, application, and interpretation rather than rote memorization. Expect scenario-based problems that test your ability to apply what you've learned.

Attempts

Best scores and quiz attempts will appear.

© 2025 ApX Machine Learning