Visualize tree structure and extract feature importances
Open
medium
track: library
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 16
- PR merge metrics
- No merged PRs in 30d
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
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