NLP101: Text Classification - Algorithms in Decision Trees

Text Classification - Algorithms in Decision Trees

par HSU09 Bùi Thị Hoài, samedi 2 mai 2026, 21:26

I am currently studying Chapter 4 - text classification and trying to understand how decision tree algorithms are used for classification tasks. In particular, I’ve come ...

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I am currently studying Chapter 4 - text classification and trying to understand how decision tree algorithms are used for classification tasks. In particular, I’ve come across three related algorithms: ID3, C4.5, and CART. I’m struggling to understand how they work, how they differ in practice, and what role Gini Impurity play in the process. Can someone explain in a simple way: 1. How Decision Trees work in general for classification tasks? 2. How do ID3, C4.5, and CART work? How do they differ in practice? 3. What is Gini Impurity? Thank you.

Re: Text Classification - Algorithms in Decision Trees

par HSU09 Mạc Hoàng Yến, dimanche 3 mai 2026, 10:32

1. How Decision Trees work (classification)

Decision Trees classify data by repeatedly splitting it based on features (e.g., words in text). Each split aims to make the ...

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1. How Decision Trees work (classification)

Decision Trees classify data by repeatedly splitting it based on features (e.g., words in text). Each split aims to make the groups more “pure” (mostly one class). The process continues until the data is well separated or stopping rules are met.

2. ID3, C4.5, and CART differences
ID3
Uses Information Gain (entropy)
No pruning → can overfit
Mainly categorical data
C4.5
Uses Gain Ratio (improves Information Gain)
Handles continuous data and missing values
Includes pruning → better generalization
CART
Uses Gini Impurity
Produces binary splits only
Works for classification and regression
Widely used in practice
3. Gini Impurity (simple meaning)

Gini Impurity measures how mixed a node is:

0 = pure (all one class)
Higher value = more mixed classes

CART chooses splits that reduce Gini impurity the most, creating cleaner groups.