Univariate Feature Selection

# Introduction

This lab demonstrates how to use univariate feature selection to improve classification accuracy on a noisy dataset. Support vector machine (SVM) is used to classify the dataset both before and after applying univariate feature selection. For each feature, we plot the p-values for the univariate feature selection and the corresponding weights of SVMs. With this, we will compare model accuracy and examine the impact of univariate feature selection on model weights.

## VM Tips

After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.