iiitl / iiitl/Decision-Tree

Analyze depth vs. overfitting trade-off

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#3 11 comments 0 reactions 0 assignees View on GitHub
medium track: optimization
Dominant language
Jupyter Notebook
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Forks
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Description

Starting from the library baseline, iterate through max_depth values from 1 to 20. Plot the training accuracy and validation accuracy on the same line graph to visualize the exact point where the model begins to overfit. Select the optimal depth.

Contributor guide

Open the contributing guide

Research direction

Start from the library baseline in the notebook and run the existing model workflow for max_depth values 1 through 20. Create the requested combined training and validation accuracy plot, then document the depth selected as optimal based on where validation performance indicates overfitting.

Written by the indexing model from the issue text.

Assessment

Tech stack
jupyter-notebook
Domain
machine-learning
Issue type
Feature
Difficulty
3/5
Estimated time
1-2 days
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
55/100

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