Главная
Study mode:
on
1
Introduction
2
Snape – Artificial Data Generator
3
Effects of Standardization
4
Effects of Encoding
5
Effects of Data Imbalance
6
Effects of Correlation
7
Variance Inflation Factor Explained
8
Dealing with Multicollinearity
9
Effects of Missing Data
10
Summary
Description:
Dive into a comprehensive 20-minute video tutorial on preprocessing data for machine learning, focusing on logistic regression. Explore the Snape artificial data generator and examine the effects of standardization, encoding, data imbalance, and correlation on your models. Learn about the Variance Inflation Factor and strategies for dealing with multicollinearity. Discover how to handle missing data effectively. Follow along with code examples available on GitHub to enhance your understanding of these crucial preprocessing techniques for logistic regression and improve your machine learning workflows.

Preprocessing Data for Machine Learning - Deep Dive

CodeEmporium
Add to list
0:00 / 0:00