Fix Information Gain Calculation in Decision Tree Algorithm#2
Open
ElmekaouiHaitham wants to merge 1 commit intoParam302:masterfrom
Open
Fix Information Gain Calculation in Decision Tree Algorithm#2ElmekaouiHaitham wants to merge 1 commit intoParam302:masterfrom
ElmekaouiHaitham wants to merge 1 commit intoParam302:masterfrom
Conversation
in the information gain formula it is the entropy, not the purity
BALAJI24092001
approved these changes
Aug 28, 2024
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This pull request corrects the formula for calculating Information Gain in the decision tree algorithm. The previous formula was incorrect as it incorrectly applied the root entropy to the weighted purities of the left and right child nodes. The corrected formula now properly calculates Information Gain by multiplying the root entropy with the weighted sum of the entropies of the left and right child nodes.