Highlights

Created a core tutorial series that represents the end-to-end modeling pipeline to build a machine learning model
Numerous improvements to visualizations in perf_plots module:
- Modified all plots to use color vision deficiency (CVD) friendly colors
- Added functions to visualize confusion matrices and model performance metrics
- Improved layout of plots produced by plot_perf_vs_epoch and plot_pred_vs_actual and added parameter to control plot size
- Reimplemented plot_prec_recall_curve to produce smoother curves.
Enhancements to multitask scaffold splitter: faster performance and optimization for response value distribution matching
Redesigned the AMPL readthedocs for easier end-user navigation.

Enhancements

Added ability to optimize multitask scaffold split for similarity of response value distributions across split subsets, using Wasserstein distance as dissimilarity metric; controlled by new parameter mtss_response_distr_weight. Improved performance of MTSS code to be much faster.
Added perf_plots functions plot_confusion_matrices, plot_model_metrics, get_metrics_from_model_pipeline and get_metrics_from_model_file to visualize and provide access to model performance metrics.
Modified plot_pred_vs_actual_from_file to make the output more consistent with plot_pred_from_actual; changed plot_pred_from_actual so that it accepts either a ModelPipeline or a model file path as its argument.
Reimplemented plot_prec_recall_curve with sklearn PrecisionRecallDisplay, with better handling of multitask models.

Fixed bug when number of scaffolds < number of superscaffolds requested
Fixed plot_pred_vs_actual_from_file so that it works on models trained with k-fold CV.
Fixed to exclude NaNs from % active calculation.