4.2 Evaluating and Tuning Models

Grokking Modern AI Fundamentals

Ask Author

Back to course home

0% completed

Vote For New Content

4.2 Evaluating and Tuning Models

Table of Contents

Contents are not accessible

Training a model is only the first step in the machine learning workflow. And evaluating how well it performs on unseen data and tuning it for better results are just as important.

Therefore, in this section, we are going to introduce common metrics (accuracy, precision, recall, F1 score), explain overfitting vs. underfitting, and show you basic hyperparameter tuning techniques—so you can refine your models systematically.

1. Accuracy, Precision, Recall, F1 Score

When you’re dealing with classification problems (like detecting spam vs

.....

Like the course? Get enrolled and start learning!

Table of Contents

Contents are not accessible