From d1872712c4ed68b8ec15a2bea60440d7d5a1e057 Mon Sep 17 00:00:00 2001 From: aaron Date: Thu, 4 Jun 2026 15:26:57 +0200 Subject: [PATCH] feautre(workshops): add workshop 6 --- SL/aufgaben/workshop6/README.md | 102 + SL/aufgaben/workshop6/data/bank_data_prep.csv | 9861 +++++++++++++++++ SL/aufgaben/workshop6/devenv.lock | 65 + SL/aufgaben/workshop6/devenv.nix | 31 + SL/aufgaben/workshop6/max_features.png | Bin 0 -> 32587 bytes .../workshop6/min_impurity_decrease.png | Bin 0 -> 32184 bytes SL/aufgaben/workshop6/n_estimators.png | Bin 0 -> 35735 bytes .../__pycache__/bfh_cas_pml.cpython-313.pyc | Bin 0 -> 6049 bytes SL/aufgaben/workshop6/src/bfh_cas_pml.py | 193 + .../workshop6/src/randomforestclassifier.py | 57 + 10 files changed, 10309 insertions(+) create mode 100644 SL/aufgaben/workshop6/README.md create mode 100644 SL/aufgaben/workshop6/data/bank_data_prep.csv create mode 100644 SL/aufgaben/workshop6/devenv.lock create mode 100644 SL/aufgaben/workshop6/devenv.nix create mode 100644 SL/aufgaben/workshop6/max_features.png create mode 100644 SL/aufgaben/workshop6/min_impurity_decrease.png create mode 100644 SL/aufgaben/workshop6/n_estimators.png create mode 100644 SL/aufgaben/workshop6/src/__pycache__/bfh_cas_pml.cpython-313.pyc create mode 100644 SL/aufgaben/workshop6/src/bfh_cas_pml.py create mode 100644 SL/aufgaben/workshop6/src/randomforestclassifier.py diff --git a/SL/aufgaben/workshop6/README.md b/SL/aufgaben/workshop6/README.md new file mode 100644 index 0000000..b7d9401 --- /dev/null +++ b/SL/aufgaben/workshop6/README.md @@ -0,0 +1,102 @@ +# Workshop 06 — RandomForestClassifier: Parameter Tuning + +## Aufgabe + +Drei Tuning-Parameter des `RandomForestClassifier` auf die erreichte Accuracy +untersuchen (vorbereiteter Bank-Datensatz, identisch zu WS5), je über einen +vorgegebenen Wertebereich: + +| Parameter | Wertebereich | Typ | +|------------------------|---------------------------|-------| +| `n_estimators` | `range(100, 500, 50)` | int | +| `max_features` | `range(1, 11)` | int | +| `min_impurity_decrease`| `np.arange(0, 0.1, 0.01)` | float | + +Zusätzlich: Wirkung von `random_state` einordnen, und (Zusatzfrage) bestimmen, +welche der übrigen Parameter keine Tuning-Parameter sind. + +## Vorgehen + +Wiederverwendung des Sweep-/Plot-Gerüsts aus WS5. Die einzige Anpassung war die +Generalisierung der Sweep-Funktion auf einen frei wählbaren Parameternamen +(`set_params` per dict-Unpacking), womit sich alle drei Parameter mit derselben +Funktion abdecken lassen. Jeder Sweep hält die übrigen Parameter auf den +Defaults und variiert nur den untersuchten. + +Pro Sweep: Liniendiagramm (Parameter vs. Accuracy) mit markiertem Maximum sowie +Konsolenausgabe von bestem Score und zugehörigem Parameterwert. + +`n_jobs=-1` gesetzt — bei bis zu 450 Bäumen pro Fit und mehreren Fits pro Sweep +ist die Parallelisierung über alle Cores hier praktisch zwingend, nicht +optional. `n_jobs` ist dabei reiner Performance-Schalter ohne Einfluss auf das +Resultat. + +## Resultate + +| Parameter | bester Score | bei Wert | +|------------------------|--------------|----------| +| `n_estimators` | 0.8792 | 400 | +| `max_features` | 0.8780 | 6 | +| `min_impurity_decrease`| 0.8750 | 0.0 | + +Interpretation der einzelnen Verläufe: + +**`n_estimators`** — die Kurve bewegt sich über den gesamten Bereich nur +zwischen 0.875 und 0.879. Diese Schwankung liegt im Bereich des Seed-Rauschens +und ist kein echtes Optimum. Die belastbare Aussage lautet: Plateau ab ~150 +Bäumen, danach reine Rechenzeit ohne Mehrwert. Der nominelle „Peak" bei 400 ist +nicht als optimaler Wert zu interpretieren. + +**`max_features`** — hier liegt echtes Signal vor: Anstieg von ~0.857 (bei 1) +auf ein breites Maximum ab ~4 Features. Der beste Wert (6) liegt nahe am +sklearn-Default `sqrt(n_features)` für Klassifikation, was den Default bestätigt. +Zu kleine Werte machen die einzelnen Bäume zu zufällig (Underfit); zu grosse +Werte erhöhen die Korrelation zwischen den Bäumen und schmälern den +Ensemble-Vorteil. + +**`min_impurity_decrease`** — bestes Resultat bei 0 (kein Pruning), danach +monotoner Abfall auf ein Plateau bei ~0.757. Das ist das aufschlussreichste +Ergebnis des Workshops und der direkte Kontrast zu WS5: beim einzelnen +DecisionTree hat Pre-Pruning das Overfitting reduziert und die Test-Accuracy +verbessert. Beim Random Forest übernimmt das Bagging diese Varianzkontrolle +bereits auf Ensemble-Ebene — Pruning der einzelnen Bäume nimmt ihnen die +gewollte Varianz und kann die Accuracy daher praktisch nur verschlechtern. + +## Wirkung von `random_state` + +Der Random Forest hat zwei Zufallsquellen: das Bootstrap-Sampling (welche Zeilen +jeder Baum sieht) und die Random Feature Selection (welche Features pro Split zur +Wahl stehen). `random_state` seedet beide und macht den Lauf reproduzierbar. + +Der Effekt des konkreten Seeds nimmt mit steigendem `n_estimators` ab: bei wenig +Bäumen schwankt die Accuracy je nach Seed merklich, bei vielen Bäumen mittelt +sich das aus. Ein Teil der Zacken im `n_estimators`-Verlauf ist genau diese +Seed-Variation und nicht echtes Signal — siehe Interpretation oben. + +## Zusatzfrage: Nicht-Tuning-Parameter + +Kriterium: ein Tuning-Parameter verschiebt den Bias-Varianz-Tradeoff bzw. die +Kapazität des Modells. Parameter, die nur Infrastruktur, Reproduzierbarkeit, +Logging, Workflow oder Reporting steuern, sind keine Tuning-Parameter. Aus +`model.get_params()` betrifft das: + +- `random_state` — Seed (Reproduzierbarkeit) +- `n_jobs` — Parallelisierung (Performance) +- `verbose` — Logging-Ausgabe +- `warm_start` — Workflow-Schalter für inkrementelles Fitten +- `oob_score` — schaltet nur die Out-of-Bag-Schätzung als Reporting an, ändert + das gefittete Modell nicht + +Grenzfälle wie `bootstrap`, `class_weight` oder `max_samples` beeinflussen das +Modell hingegen sehr wohl und zählen damit zu den Tuning-Parametern. + +## Caveats / Deviations + +- **One-at-a-time-Tuning**: jeder Parameter wurde einzeln bei Defaults der + übrigen variiert. Damit werden Wechselwirkungen zwischen Parametern nicht + erfasst; das gemeinsame Optimum kann von der Kombination der drei + Einzelbestwerte abweichen. Eine gemeinsame Suche (`GridSearchCV`, vgl. WS4) + wäre dafür das richtige Werkzeug. +- **Optimistic Bias**: wie in WS4/WS5 wird direkt gegen das Test-Set getunt. Die + berichteten Bestwerte sind dadurch optimistisch verzerrt; sauber wäre ein + Validierungs-Split bzw. 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"root", + "version": 7 +} \ No newline at end of file diff --git a/SL/aufgaben/workshop6/devenv.nix b/SL/aufgaben/workshop6/devenv.nix new file mode 100644 index 0000000..22ff0ea --- /dev/null +++ b/SL/aufgaben/workshop6/devenv.nix @@ -0,0 +1,31 @@ +{ pkgs, ... }: + +{ + # Native libs that the pip-wheel-installed numpy/scipy/matplotlib stack + # dlopen()s at runtime. zlib war schon in W3/W4 nötig (libz.so.1), + # stdenv.cc.cc.lib liefert libstdc++ für die scipy/sklearn-Wheels. + packages = [ + pkgs.zlib + pkgs.stdenv.cc.cc.lib + ]; + + languages.python = { + enable = true; + venv.enable = true; + venv.requirements = '' + pandas + numpy + scikit-learn + matplotlib + seaborn + ''; + }; + + # Loader-Pfad für die obigen nativen Libs. 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sns.lineplot(x=params, y=scores) + plt.scatter(best_p, best_s, color="black", zorder=5) # Peak markieren + plt.xlabel(name) + plt.ylabel("accuracy") + plt.title("random forest parameter tuning") + plt.savefig(f"{name}.png", dpi=120, bbox_inches="tight") + + +def main(): + X_train, X_test, y_train, y_test = prep_data(str(DATA), "y", seed=1234) + + sweeps = [ + ("n_estimators", range(100, 500, 50)), + ("max_features", range(1, 11)), + ("min_impurity_decrease", np.arange(0, 0.1, 0.01)), + ] + + for name, params in sweeps: + scores = sweep(name, params, X_train, y_train, X_test, y_test) + report(params, scores, name) + + +if __name__ == "__main__": + main()