Decision tree classifier in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Decision Tree Classifier?

A Decision Tree Classifier is a simple model that splits data into branches based on questions about features, helping to classify data into categories.

beginner

What does each node in a decision tree represent?

Each node represents a question or test on a feature that splits the data into groups to help decide the final class.

intermediate

How does a decision tree decide where to split the data?

It chooses splits that best separate the classes, often using measures like Gini impurity or entropy to find the most informative questions.

intermediate

What is overfitting in decision trees and how can it be prevented?

Overfitting happens when the tree is too complex and fits noise, not just patterns. It can be prevented by limiting tree depth, requiring a minimum number of samples to split, or pruning.

beginner

What is the output of a decision tree classifier?

The output is the predicted class label for a given input, based on the path through the tree from root to a leaf node.

What does a leaf node in a decision tree represent?

A A final class prediction

B A question about a feature

C A data split point

D An error in the model

Which measure is commonly used to decide the best split in a decision tree?

A Gini impurity

B Mean squared error

C Euclidean distance

D Cosine similarity

What problem occurs if a decision tree is too deep?

A Underfitting

B Faster training

C Overfitting

D Less accuracy

How can you reduce overfitting in decision trees?

A Increase tree depth

B Use more features

C Ignore data cleaning

D Limit tree depth

What type of problems is a decision tree classifier used for?

A Regression problems

B Classification problems

C Clustering problems

D Dimensionality reduction

Explain how a decision tree classifier makes predictions from input data.

Describe two ways to prevent overfitting in decision tree classifiers.