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Tutorials

Learning-oriented walkthroughs that guide you from zero to confident usage.

Available

  • Basic CATDAP — The original CATDAP-01 / CATDAP-02 walkthrough on the UCI Heart Disease dataset
  • EDA on Titanic — v0.3+ univariate EDA APIs (describe, plot_variable, plot_missing) demonstrated on the Titanic dataset
  • AIC-optimal binning on iris — Why CATDAP-02's pooling search beats equal-width binning, with cut points visualized against species histograms
  • Multivariate scale (synthetic) — Scale to 56 binary variables on a synthetic wide dataset, use catdap1 to rank candidates and catdap2(nvar=k) to keep the subset search tractable
  • Real-world EDA on seaborn Titanic — End-to-end workflow on messy data: missingness inspection, cleanup, mixed-pooling CATDAP-02, and HTML/Plotly export. Requires pycatdap[tutorial].
  • Target-pair analysis — Drill into a single (target, explanatory) pair with target_summary and plot_target: four-perspective tables, AIC-optimal binning for continuous explanatories, ΔAIC ranking, Plotly export, and continuous-target regression mode via RegressionTargetSummary (H-0005). Requires pycatdap[tutorial].
  • Bivariate association analysis — Step beyond a single pair: plot_pair for symmetric two-column plots, aic_heatmap for one-glance ΔAIC matrices, association_matrix for the full m × m ΔAIC sweep, and association_plot for vcd-style Pearson residuals. Requires pycatdap[tutorial].
  • One-call profile() report — The v0.5.0 flagship API: pycatdap.profile(df, response=...) bundles overview, variable cards, ΔAIC association matrix, CATDAP-02 top subsets, and quality warnings into a single call, then renders a self-contained HTML report with inline Plotly figures (H-0007). Requires pycatdap[plotly] and pycatdap[tutorial].
  • Target analysis & quality suite — The v0.6.0 target-driven additions: quality_report(df) for a fast CI-friendly quality scan, target_analysis(df, response) for ΔAIC ranking + top-K cross-tabs, pycatdap.measures.* (aic / cramers_v / mutual_info + register), association_matrix(measure=...) extension, and pycatdap.suite.AICIndependenceSuite for the assert suite_result.passed, suite_result.summary() CI idiom. Requires pycatdap[plotly] and pycatdap[tutorial].
  • ML error labelling — The v0.7.0 ML-error building blocks: error_label, confusion_label, residual_label, and abs_residual_pool turn model predictions into CATDAP-ready categorical responses.
  • One-call ML error analysis — The v0.8.0 wrapper: error_analysis(df, y_true, y_pred) returns an ErrorAnalysisResult with ΔAIC feature ranking, single-variable slices, and confusion/residual summaries.
  • ML error visualisation — The v0.9.0 visualisation layer: plot_confusion / plot_confusion_by_slice / confusion_aic (classification) and residual_plot / residual_by_category / residual_pool_plot (regression), plus ErrorAnalysisResult delegation methods on both backends.
  • ML calibration — The v0.10.0 calibration toolkit: calibration_curve reliability diagram with AIC-optimal probability binning + Wilson CIs, brier_score / expected_calibration_error / maximum_calibration_error, and the ErrorAnalysisResult.calibration_curve delegation (binary classification). Requires pycatdap[plotly] and pycatdap[tutorial].
  • ML slice discovery & drift — The v0.11.0 subgroup toolkit: discover_error_slices finds multi-variable cohorts where model errors concentrate (support-pruned search, pluggable interestingness measure), plus compare_cohorts and detect_drift for distribution/relationship shifts between datasets.