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/
optimize-random-forest
/ 0.0.1
Optimize Random Forest with Optuna on Zarr Data
A solution that optimizes a Random Forest model using Optuna, data from a Zarr zip store, and performs 10-fold cross-validation.
Tags
imaging
cryoet
Python
napari
cellcanvas
Solution written by
Kyle Harrington
License of solution
MIT
Source Code
View on GitHub
Arguments
--input_zarr_path
Path to the input Zarr zip store containing the features and labels. (default value: PARAMETER_VALUE)
--output_model_path
Path for the output joblib file containing the trained Random Forest model. (default value: PARAMETER_VALUE)
--best_params_path
Path for the output file containing the best parameters from Optuna. (default value: PARAMETER_VALUE)
--n_splits
Number of splits for cross-validation. (default value: PARAMETER_VALUE)
--subset_size
Total number of points for balanced subset. (default value: PARAMETER_VALUE)
--seed
Random seed for reproducibility. (default value: PARAMETER_VALUE)
Usage instructions
Please follow
this link
for details on how to install and run this solution.