From 8155beb7741ce2dc9669649611cd1f27c3f43a6e Mon Sep 17 00:00:00 2001 From: Joaquin Torres Date: Fri, 28 Jun 2024 17:34:49 +0000 Subject: [PATCH] Update README.md --- README.md | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 7b7bcf5..2ebac73 100644 --- a/README.md +++ b/README.md @@ -5,12 +5,20 @@ This repository is organized as follows: * [EDA](./EDA): * [output](./EDA/output): Plots about feature distributions, correlations and importance. - * [EDA.ipynb](./EDA/EDA.ipynb): Notebook used for exploring and filtering data, handling missing values, encoding variables, building the final pre- and post- pandemic datasets, and generating plots for feature distributions, correlations and importance. -* [gen_train_data](./gen_train_data): Generating training and testing datasets [wait for final approach TBC]. -* [model_selection](./model_selection): Tuning models, generating metrics from cross-validation and testing, and selecting final models. - * [hyperparam_tuning.py](./model_selection/hyperparam_tuning.py): Tunes models through a random search of hyperparameters. - * [cv_metric_gen.py](./model_selection/cv_metric_gen.py): Generates cross-validation metrics and plots for the tuned models. - * [cv_metrics_distr.py](./model_selection/cv_metrics_distr.py): -* [explainability](./explicability): SHAP explainability analysis, comparing pre- and post-pandemic groups. + * [EDA.ipynb](./EDA/EDA.ipynb): Exploring and filtering data, handling missing values, encoding variables, building the final pre- and post- pandemic datasets, and generating plots for feature distributions, correlations and importance. +* [gen_train_data](./gen_train_data): + * [gen_train_data.ipynb](./gen_train_data/gen_train_data.ipynb): Generating training and testing datasets. +* [model_selection](./model_selection): + * [hyperparam_tuning.py](./model_selection/hyperparam_tuning.py): Tuning models through a random search of hyperparameters. + * [cv_metric_gen.py](./model_selection/cv_metric_gen.py): Generating cross-validation metrics and plots for each of the tuned models. + * [cv_metrics_distr.py](./model_selection/cv_metrics_distr.py): Generating boxplots for each cross-validation metric and tuned model. + * [test_models.py](./model_selection/test_models.py): Testing tuned models with test dataset. +* [explainability](./explainability): + * [fit_final_models.py](./explainability/fit_final_models.py): Saving fitted model for each selected final model. + * [compute_shap_vals.py](./explainability/compute_shap_vals.py): Computing SHAP values for final models. + * [compute_shap_inter_vals.py](./explainability/compute_shap_inter_vals.py): Computing SHAP interaction values for final models. + * [shap_plots.py](./explainability/shap_plots.py): Generating SHAP summary plots for the SHAP and SHAP interaction values computed. Comparing major differences between pre- and post-pandemic groups. + * [output](./explainability/output): + * [plots](./explainability/output/plots): SHAP and SHAP interaction summary plots. -[Outro] +[Outro] \ No newline at end of file -- 2.24.1