Module 18: Evaluate Regression Models with Python

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

Exercise Overview

In this module, you will learn:

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

This module focuses on practical techniques for evaluating regression models, enabling you to assess model performance, identify areas for improvement, and optimize model parameters.

Jupyter Notebooks

To get started, access the following Jupyter notebooks:

1. Train and Evaluate a Regression Model - Learn how to train and evaluate a regression model using Scikit-Learn, including techniques for assessing model performance and interpreting evaluation metrics.
2. Experiment with More Powerful Regression Models - Explore more advanced regression models, such as Lasso algorithm, Decision Tree algorithm, or Ensemble algorithm, and experiment with their performance on different datasets.
3. Optimize and Save Models - Discover techniques for optimizing regression models by fine-tuning hyperparameters and saving trained models for future use.

These notebooks will guide you through practical exercises where you'll apply regression model evaluation techniques, experiment with advanced models, and optimize model performance using Scikit-Learn.

Happy learning!