Module 20: Evaluate Classification Models with Python

In this exercise, you’ll delve into the evaluation of classification models in machine learning using Python. Through practical exercises, you'll learn how to effectively train, evaluate, and optimize classification models using the Scikit-Learn framework.

Exercise Overview

In this module, you will learn:

• How to train and evaluate classification models using the Scikit-Learn framework.

This module focuses on practical techniques for evaluating classification models, enabling you to assess model performance, identify areas for improvement, and select the best-performing model for your dataset.

Jupyter Notebooks

To get started, access the following Jupyter notebooks:

1. Evaluate a Classification Model - Learn how to evaluate the performance of a classification model using various evaluation metrics such as accuracy, precision, recall, and F1-score.
2. Perform Classification with Alternative Metrics - Explore alternative evaluation metrics for classification models and compare their performance using different classification algorithms.
3. Evaluate a Multiclass Classification Model - Dive into evaluating multiclass classification models and learn techniques for assessing model performance in scenarios with multiple classes.

These notebooks will guide you through practical exercises where you'll apply classification model evaluation techniques, experiment with alternative evaluation metrics, and evaluate multiclass classification models using Scikit-Learn.

Happy learning!