pip install MLLytics
or
python setup.py install
or
conda env create -f environment.yml
- Comment all functions and classes
- Add type hinting to all functions and classes (https://mypy.readthedocs.io/en/latest/cheat_sheet_py3.html)
- Scoring functions
- More output stats in overviews
- Update reliability plot https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/
- Tests
- Switch from my metrics to sklearn metrics where it makes sense? aka
fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])and more general macro/micro average metrics from: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score - Additional metrics (sensitivity, specificity, precision, negative predictive value, FPR, FNR, false discovery rate, accuracy, F1 score
- Fix size of confusion matrix
- Check works with matplotlib 3
- Tidy up legends and annotation text on plots
- Joy plots
- Brier score for calibration plot
- Tidy up cross validation and plots (also repeated cross-validation)
- Acc-thresholds graph
Allow figure size and font sizes to be passed into plotting functionsExample guides for each function in jupyter notebooksMultiClassMetrics class to inherit from ClassMetrics and share common functionsREGRESSION
- Scott Clay
- David Sullivan