iiitl / iiitl/Decision-Tree

Visualize tree structure and extract feature importances

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#8 24 comments 0 reactions 0 assignees View on GitHub
medium track: library
Dominant language
Jupyter Notebook
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0
Forks
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Description

Using the baseline library model, plot the top 3 levels of the decision tree and extract the feature importances. Create a bar chart of the top 5 most important features and explain if they align with the insights from your earlier data exploration.

Contributor guide

Open the contributing guide

Research direction

Start from the notebook and baseline library model used for the earlier data exploration. Run the existing analysis, then produce a plot of the tree's top three levels and a bar chart for its five highest feature importances. Done means both visualizations are included and the results are explained against the earlier insights.

Written by the indexing model from the issue text.

Assessment

Tech stack
jupyter-notebook
Domain
data-visualization, 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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