smazzanti

mrmr

mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.

are_you_still_using_elbow_method

tds_black_box_models_more_explainable

Jupyter Notebook used for writing the article "Black-Box models are actually more explainable than a Logistic Regression" published in Towards Data Science: https://towardsdatascience.com/black-box-models-are-actually-more-explainable-than-a-logistic-regression-f263c22795d

tds_features_important_doesnt_mean_good

valicast

Validation for forecasts

confusion_viz

Interactive visualization of the output of any binary classifier.

general_vs_specialized_models

beyond_one_hot

Comparing 17 types of encoding for categorical variables

standardized_wasserstein_distance

few_vital_causes

tds_approximate_predictions_for_feature_selection

correlation_explained_visually

tds_best_or_luckiest

tds_thompson_sampling

category_encoders

A library of sklearn compatible categorical variable encoders

cqr

Conformalized Quantile Regression

direct_loss_estimation

MAPIE

A scikit-learn-compatible module for estimating prediction intervals.

misc

nonconformist

Python implementation of the conformal prediction framework.

tds-scraping

tds_are_outliers_harder_to_predict

tds_your_dataset_has_missing_values_do_nothing

git_tutorial

lifetime_value

mlops

rbs

scikit-learn

scikit-learn: machine learning in Python

shap

A game theoretic approach to explain the output of any machine learning model.

tds_feature_selection_you_are_probably_doing_it_wrong