Classification with PyCaret

Explore how to perform classification in machine learning using the PyCaret library in Python. Understand logistic regression, other classification models, and apply exploratory data analysis on datasets like Iris to build and evaluate effective models.

We'll cover the following...

Classification methods in PyCaret
Importing the necessary libraries
Loading the dataset

Classification is one of the fundamental supervised learning tasks. Its goal is to predict a categorical variable known as the class label. This task is known as binary classification when there are only two classes ( $0$ and $1$ ), or multiclass classification in case there are more. One of the most widely used binary classification models is logistic regression. It is defined in the following equation:

\log (\frac{p_{n}}{1-p_{n}})=\beta_{0}+\beta_{1} x_{n 1}+\cdots+\beta_{p} x_{n p}=\beta^{T} X_{n}

1.Introduction to Machine Learning

2.Regression

3.Classification

4.Clustering

Project

5.Anomaly Detection

6.Natural Language Processing

7.Deploying a Machine Learning Model

8.Conclusion

9.Appendix

Classification with PyCaret