Modeling multiclass classifications are common in data science. In this video, learn how to create a logistic regression model for multiclass classification using the Python library scikit-learn.
- [Instructor] A lot of classification models … like logistic regression … were originally designed for binary classification. … That's predicting whether something's one thing or another. … For datasets with more than two classes, … what do you do? … For multi-class classification problems, … one approach is to split the task … into multiple binary classification datasets, … and fit a binary classification model on each. … In this video, we'll explore the one-vs-rest strategy … and how you can apply it … to logistic regression using scikit-learn. … One-vs-rest, which is also sometimes called one-vs-all … is a technique that extends binary classifiers … to multi-class problems. … Here's how it works. … You train one classifier per class … where one class is treated as the positive class. … And the other classes are considered negative classes. … For example, say you have an image recognition task. … Your dataset has four classes … the digits zero, one, two, and three. … Your goal is to classify them. …
This course was created by Madecraft. We are pleased to host this content in our library.
- Why use scikit-learn?
- Supervised vs. unsupervised learning
- Linear and logistic regression
- Decision trees and random forests
- K-means clustering
- Principal component analysis (PCA)