cellcanvas / train-model / 0.1.1

Train Random Forest on Zarr Data with Cross-Validation

A solution that trains a Random Forest model using data from a Zarr zip store, filters runs with only one label, and performs 10-fold cross-validation.

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)

--n_estimators

Number of trees in the Random Forest. (default value: PARAMETER_VALUE)

--min_feature_size

Minimum feature size to include in the training data. (default value: PARAMETER_VALUE)

Usage instructions

Please follow this link for details on how to install and run this solution.