GitHub - kwkushan/effects-of-tuning-hyperparameters-in-random-forest-regression-on-reservoir-s-porosity-prediction: These codes were developed as a part of a research where effects of three commonly used hyperparameters of random forest regression, namely, n_estimators, max_features and min_samples_leaf were investigated.

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
RFR_Code_max_features_Approach_1.ipynb		RFR_Code_max_features_Approach_1.ipynb
RFR_Code_max_features_and_min_samples_leaf_Approach_2.ipynb		RFR_Code_max_features_and_min_samples_leaf_Approach_2.ipynb
RFR_Code_min_samples_leaf_Approach_1.ipynb		RFR_Code_min_samples_leaf_Approach_1.ipynb
RFR_Code_n_est_and_max_features_Approach_2.ipynb		RFR_Code_n_est_and_max_features_Approach_2.ipynb
RFR_Code_n_est_and_min_samples_leaf_Approach_2.ipynb		RFR_Code_n_est_and_min_samples_leaf_Approach_2.ipynb
RFR_Code_n_est_max_features_and_min_sample_leaf_Approach_3.ipynb		RFR_Code_n_est_max_features_and_min_sample_leaf_Approach_3.ipynb
RFR_Code_n_estimators_Approach_1.ipynb		RFR_Code_n_estimators_Approach_1.ipynb

About

These codes were developed as a part of a research where effects of three commonly used hyperparameters of random forest regression, namely, n_estimators, max_features and min_samples_leaf were investigated.