Analyze depth vs. overfitting trade-off
Open
medium
track: optimization
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 16
- PR merge metrics
- No merged PRs in 30d
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
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