{ "cells": [ { "cell_type": "markdown", "id": "7ba05a39", "metadata": {}, "source": [ "# Simulationsstudie: Diebstahlerkennung eines Autos in Echtzeit\n", "\n", "**Name:** Yann Ahlgrim \n", "\n", "In dieser Simulationsstudie wird ein Modell entwickelt, das anhand von Fahrverhaltensdaten erkennt, ob ein anderer Fahrer als üblich am Steuer sitzt. Ziel ist es, eine Wahrscheinlichkeit für einen Fahrerwechsel zu schätzen und bei hoher Wahrscheinlichkeit eine Diebstahlwarnung auszugeben. Die Studie umfasst die Generierung einer synthetischen Grundgesamtheit, die Simulation der Data Scientist Perspektive und die Analyse der Modellgüte." ] }, { "cell_type": "markdown", "id": "f04292ef", "metadata": {}, "source": [ "## Erzeugung der Grundgesamtheit" ] }, { "cell_type": "markdown", "id": "a5fe9381", "metadata": {}, "source": [ "### Datengrundlage\n", "\n", "#### Erklärende Variablen:\n", "\n", "1. **Durchschnittsgeschwindigkeit (metrisch):** \n", " Die Werte verteilen sich natürlicherweise ungefähr normal um ca. 47 km/h (29 mph) mit großer Streuung. Aggressive Fahrer tendieren zu höheren Durchschnittsgeschwindigkeiten. Diese Variable ist erklärend, da ein anderer Fahrer oft abweichende Geschwindigkeitsprofile aufweist. \n", " → Die Verteilung basiert auf: [NHTSA 100-Car Naturalistic Driving Study](https://www.nhtsa.gov/sites/nhtsa.gov/files/100carmain.pdf)\n", "\n", "2. **Schaltverhalten (ordinal):** \n", " Charakteristisches Schaltmuster bei Handschaltgetrieben, insbesondere der Drehzahlbereich beim Hochschalten. Fahrer unterscheiden sich deutlich: \n", " - *frueh* (ökonomisches Fahren), \n", " - *normal*, \n", " - *spaet* (sportliches/aggressives Fahren). \n", " Diese Variable dürfte prädiktiv sein, da Schaltgewohnheiten fahrerspezifisch sind. Studien zeigen, dass sie zur Fahreridentifikation genutzt werden können. \n", " → Quelle: [Driver Identification Based on Gear Shift Events and Attention-Based Bidirectional Long Short-Term Memory for Manual Transmission System)](https://ieeexplore.ieee.org/document/10034086)\n", "\n", "3. **Harte Bremsmanöver (metrisch):** \n", " Anzahl starker Bremsvorgänge mit hoher Verzögerung (>0,3 g). Modellierung z.B. als Poisson-verteilte Zufallsvariable mit geringem Mittelwert (wenige Ereignisse pro 100 km). Harte Bremsmanöver korrelieren mit aggressiver Fahrweise und sind prädiktiv für Fahrerwechsel. \n", " → Quelle: [Tesla Safety Score Definition](https://www.findmyelectric.com/blog/tesla-safety-score-beta-explained)\n", "\n", "4. **Geschwindigkeitsüberschreitungen (ordinal, 3 Kategorien):** \n", " haeufigkeit bzw. Ausmaß, mit dem Tempolimits überschritten werden. Kategorisierung z.B. in: \n", " - *selten*, \n", " - *manchmal*, \n", " - *haeufig* zu schnell. \n", " Diese Variable ist prädiktiv, da ein fremder Fahrer oft ein anderes Risikoverhalten beim Speeding zeigt.\n", " Laut einer AAA-Verkehrssicherheitsstudie geben nahezu 50 % der Fahrer an, auf Autobahnen im letzten Monat ~15 mph (~24 km/h) über dem Tempolimit gefahren zu sein.\n", " → Quelle: [87 Percent of Drivers Engage in Unsafe Behaviors While Behind the Wheel](https://newsroom.aaa.com/2016/02/87-percent-of-drivers-engage-in-unsafe-behaviors-while-behind-the-wheel/)\n", "\n", "---\n", "\n", "#### Nicht erklärende Variablen:\n", "\n", "\n", "5. **Wetterbedingungen (nominal, 3 Kategorien):** \n", " - *trocken* (~70–75 %) \n", " - *nass* (~20–25 %) \n", " - *winterlich* (<5 %) \n", " → Modelliert als Multinomial. Kontextvariable. \n", " Die Einteilung basiert auf Daten des Statistischen Bundesamtes: Etwa 70–75 % aller Fahrten finden unter trockenen Bedingungen statt, 20–25 % bei Nässe und unter 5 % bei Schnee oder Glätte.\n", "\n", "6. **Fahrstrecke (metrisch):** \n", " Länge der Fahrt (z.B. lognormalverteilt um 12 km). Kontextvariable, nicht direkt erklärend. \n", " → Quelle: [Mobilität in Deutschland 2017 – Ergebnisbericht](https://www.bmv.de/SharedDocs/DE/Anlage/G/mid-ergebnisbericht.pdf)\n", "\n", " \n", "7. **Straßentyp (nominal, 3 Kategorien):** \n", " - *Autobahn* (~30–33 %) \n", " - *Außerorts* (~43 %) \n", " - *Innerorts* (~24–27 %) \n", " → Modelliert als Multinomialverteilung. Kontextvariable ohne direkte Prädiktionswirkung. \n", " → Quelle: [Klimaschutzinstrumente im Verkehr](https://openumwelt.de/server/api/core/bitstreams/07bd23f9-ff0a-41e5-b89b-8d7baf0a5c36/content)\n", "\n", "8. **Wochentag (nominal, 2–3 Kategorien):** \n", " Z.B. *Werktag* vs *Wochenende* oder *Mo–Fr*, *Sa*, *So*. Modellierung als kategorische Variable. Kontextvariable. \n", " → Quelle: [Mobilität in Deutschland 2017 – Ergebnisbericht](https://www.bmv.de/SharedDocs/DE/Anlage/G/mid-ergebnisbericht.pdf)\n" ] }, { "cell_type": "markdown", "id": "1468207b", "metadata": {}, "source": [ "### Bibliotheken Imports" ] }, { "cell_type": "code", "execution_count": 1, "id": "97ba29a9", "metadata": {}, "outputs": [], "source": [ "# Import der benötigten Bibliotheken\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from scipy.stats import norm, multinomial, poisson, lognorm, bernoulli\n", "import statsmodels.api as sm\n", "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", "\n", "np.random.seed(11)\n", "N = 1_000_000" ] }, { "cell_type": "markdown", "id": "27947656", "metadata": {}, "source": [ "### Verteilungen der Variablen" ] }, { "cell_type": "code", "execution_count": 2, "id": "61247da5", "metadata": {}, "outputs": [], "source": [ "# 1. Durchschnittsgeschwindigkeit (metrisch, normalverteilt)\n", "avg_speed = np.clip(np.random.normal(loc=47, scale=10, size=N), 10, 130)\n", "\n", "# 2. Schaltverhalten (ordinal, 3 Kategorien)\n", "shift_behavior = np.random.choice(['frueh', 'normal', 'spaet'], size=N, p=[0.4, 0.4, 0.2])\n", "\n", "# 3. Harte Bremsmanöver (metrisch, Poisson)\n", "hard_brakes = np.random.poisson(lam=2, size=N)\n", "\n", "# 4. Geschwindigkeitsüberschreitungen (ordinal, 3 Kategorien) – Abhängigkeit von avg_speed\n", "def softmax(x):\n", " e_x = np.exp(x - np.max(x, axis=1, keepdims=True))\n", " return e_x / e_x.sum(axis=1, keepdims=True)\n", "\n", "speed_scaled = (avg_speed - avg_speed.min()) / (avg_speed.max() - avg_speed.min())\n", "strength = 0.18\n", "logits = np.zeros((N, 3))\n", "logits[:, 0] = 1 - strength * speed_scaled # selten\n", "logits[:, 1] = 1 # manchmal\n", "logits[:, 2] = 1 + strength * speed_scaled # haeufig\n", "probs = softmax(logits)\n", "speeding = [np.random.choice(['selten', 'manchmal', 'haeufig'], p=p) for p in probs]\n", "\n", "# 5. Wetterbedingungen (nominal, Kontext)\n", "weather = np.random.choice(['trocken', 'nass', 'winterlich'], size=N, p=[0.75, 0.2, 0.05])\n", "\n", "# 6. Fahrstrecke (metrisch, lognormal)\n", "mu, sigma = np.log(12), 0.7\n", "trip_distance = np.random.lognormal(mean=mu, sigma=sigma, size=N)\n", "\n", "# 7. Straßentyp (nominal, Kontext) – Abhängigkeit von trip_distance mit Softmax-Funktion\n", "# Skaliere trip_distance auf [0, 1]\n", "dist_scaled = (trip_distance - trip_distance.min()) / (trip_distance.max() - trip_distance.min())\n", "road_strength = 0.18\n", "road_logits = np.zeros((N, 3))\n", "road_logits[:, 0] = 1 - road_strength * dist_scaled # Innerorts\n", "road_logits[:, 1] = 1 # Außerorts\n", "road_logits[:, 2] = 1 + road_strength * dist_scaled # Autobahn\n", "road_probs = softmax(road_logits)\n", "road_type = [np.random.choice(['Innerorts', 'Außerorts', 'Autobahn'], p=p) for p in road_probs]\n", "\n", "# 8. Wochentag (nominal, Kontext)\n", "weekday = np.random.choice(['Mo-Fr', 'Sa', 'So'], size=N, p=[0.7, 0.15, 0.15])" ] }, { "cell_type": "markdown", "id": "defebcc1", "metadata": {}, "source": [ "### Zusammenführen der Daten in ein DataFrame" ] }, { "cell_type": "code", "execution_count": 3, "id": "f8e0efb0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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avg_speedshift_behaviorhard_brakesspeedingweathertrip_distanceroad_typeweekday
064.494547frueh2manchmaltrocken30.449962InnerortsMo-Fr
144.139270frueh3haeufigtrocken11.911103AutobahnMo-Fr
242.154349spaet2seltentrocken6.086055AutobahnMo-Fr
320.466814normal5haeufigtrocken5.732684InnerortsMo-Fr
446.917154spaet2seltentrocken7.256758AußerortsMo-Fr
\n", "
" ], "text/plain": [ " avg_speed shift_behavior hard_brakes speeding weather trip_distance \\\n", "0 64.494547 frueh 2 manchmal trocken 30.449962 \n", "1 44.139270 frueh 3 haeufig trocken 11.911103 \n", "2 42.154349 spaet 2 selten trocken 6.086055 \n", "3 20.466814 normal 5 haeufig trocken 5.732684 \n", "4 46.917154 spaet 2 selten trocken 7.256758 \n", "\n", " road_type weekday \n", "0 Innerorts Mo-Fr \n", "1 Autobahn Mo-Fr \n", "2 Autobahn Mo-Fr \n", "3 Innerorts Mo-Fr \n", "4 Außerorts Mo-Fr " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame({\n", " 'avg_speed': avg_speed,\n", " 'shift_behavior': shift_behavior,\n", " 'hard_brakes': hard_brakes,\n", " 'speeding': speeding,\n", " 'weather': weather,\n", " 'trip_distance': trip_distance,\n", " 'road_type': road_type,\n", " 'weekday': weekday\n", "})\n", "\n", "df.head()\n" ] }, { "cell_type": "markdown", "id": "f06e7c4d", "metadata": {}, "source": [ "### VIF der Abhängigkeiten prüfen" ] }, { "cell_type": "code", "execution_count": 4, "id": "6bfb199a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Variance Inflation Factor (VIF) für ausgewählte Variabeln:\n", " features VIF Factor\n", "0 avg_speed 2.570716\n", "1 speeding_manchmal 1.819868\n", "2 speeding_selten 1.750848\n" ] } ], "source": [ "vif = pd.DataFrame()\n", "columns = ['avg_speed', 'speeding']\n", "vif_df = pd.get_dummies(df[columns], drop_first=True)\n", "vif_df = vif_df.astype(float)\n", "vif['features'] = vif_df.columns\n", "vif['VIF Factor'] = [variance_inflation_factor(vif_df.values, i) for i in range(vif_df.shape[1])]\n", "vif = vif.sort_values('VIF Factor', ascending=False).reset_index(drop=True)\n", "print(\"\\nVariance Inflation Factor (VIF) für ausgewählte Variabeln:\")\n", "print(vif)" ] }, { "cell_type": "code", "execution_count": 5, "id": "1d3df64a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Variance Inflation Factor (VIF) für ausgewählte Variabeln:\n", " features VIF Factor\n", "0 trip_distance 1.677148\n", "1 road_type_Außerorts 1.342378\n", "2 road_type_Innerorts 1.334770\n" ] } ], "source": [ "vif = pd.DataFrame()\n", "columns = ['road_type', 'trip_distance']\n", "vif_df = pd.get_dummies(df[columns], drop_first=True)\n", "vif_df = vif_df.astype(float)\n", "vif['features'] = vif_df.columns\n", "vif['VIF Factor'] = [variance_inflation_factor(vif_df.values, i) for i in range(vif_df.shape[1])]\n", "vif = vif.sort_values('VIF Factor', ascending=False).reset_index(drop=True)\n", "print(\"\\nVariance Inflation Factor (VIF) für ausgewählte Variabeln:\")\n", "print(vif)" ] }, { "cell_type": "code", "execution_count": 6, "id": "49045ee5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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avg_speedshift_behaviorhard_brakesspeedingweathertrip_distanceroad_typeweekday
064.494547frueh2manchmaltrocken30.449962InnerortsMo-Fr
144.139270frueh3haeufigtrocken11.911103AutobahnMo-Fr
242.154349spaet2seltentrocken6.086055AutobahnMo-Fr
320.466814normal5haeufigtrocken5.732684InnerortsMo-Fr
446.917154spaet2seltentrocken7.256758AußerortsMo-Fr
\n", "
" ], "text/plain": [ " avg_speed shift_behavior hard_brakes speeding weather trip_distance \\\n", "0 64.494547 frueh 2 manchmal trocken 30.449962 \n", "1 44.139270 frueh 3 haeufig trocken 11.911103 \n", "2 42.154349 spaet 2 selten trocken 6.086055 \n", "3 20.466814 normal 5 haeufig trocken 5.732684 \n", "4 46.917154 spaet 2 selten trocken 7.256758 \n", "\n", " road_type weekday \n", "0 Innerorts Mo-Fr \n", "1 Autobahn Mo-Fr \n", "2 Autobahn Mo-Fr \n", "3 Innerorts Mo-Fr \n", "4 Außerorts Mo-Fr " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "id": "1f6ee727", "metadata": {}, "source": [ "### Zielvariable erzeugen" ] }, { "cell_type": "code", "execution_count": 7, "id": "75318d77", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_15840\\2403674952.py:43: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.countplot(x=target, palette='viridis')\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "shift_dummies = pd.get_dummies(shift_behavior, prefix='shift')\n", "speeding_dummies = pd.get_dummies(speeding, prefix='speeding')\n", "weather_dummies = pd.get_dummies(weather, prefix='weather')\n", "road_dummies = pd.get_dummies(road_type, prefix='road')\n", "weekday_dummies = pd.get_dummies(weekday, prefix='weekday')\n", "\n", "X = pd.DataFrame({\n", " 'avg_speed': avg_speed,\n", " 'hard_brakes': hard_brakes,\n", " 'trip_distance': trip_distance\n", "})\n", "X = pd.concat([X, shift_dummies, speeding_dummies, weather_dummies, road_dummies, weekday_dummies], axis=1)\n", "\n", "# Betas reduziert, damit die Zielvariable nicht zu häufig 1 ist\n", "betas = np.array([\n", " 0.015, # avg_speed\n", " 0.1, # hard_brakes\n", " 0., # trip_distance\n", " 0.05, 0.075, # shift_dummies\n", " 0.05, 0.075, # speeding_dummies\n", " 0., 0., # weather_dummies\n", " 0., 0., # road_dummies\n", " 0., 0. # weekday_dummies\n", "])\n", "\n", "# Sicherstellen, dass die Dimension passt\n", "X_model = X.iloc[:, :len(betas)]\n", "\n", "X_model = X_model.astype(float)\n", "rauschen = np.random.normal(0, 0.2, size=N)\n", "\n", "# Linearkombination + Rauschen + Offset (damit die Zielvariable nicht zu häufig 1 ist)\n", "lin_comb = X_model.values @ betas + rauschen - 2\n", "\n", "# Logistische Funktion → Wahrscheinlichkeiten\n", "pi = 1 / (1 + np.exp(-lin_comb))\n", "\n", "# Zielvariable simulieren\n", "target = np.random.binomial(1, pi)\n", "\n", "# Visualisierung der Verteilung der Zielvariable\n", "plt.figure(figsize=(10, 6))\n", "sns.countplot(x=target, palette='viridis')\n", "plt.title('Verteilung der Zielvariable (Diebstahl: ja/nein)')\n", "plt.xlabel('Diebstahl')\n", "plt.ylabel('Anzahl')\n", "plt.xticks([0, 1], ['Nein', 'Ja'])\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "825cc812", "metadata": {}, "source": [ "### Verteilungen der Variablen\n", "\n", "- **Durchschnittsgeschwindigkeit (`avg_speed`)**: normalverteilt mit Mittelwert 47 km/h und Standardabweichung 10, begrenzt auf den Bereich [10, 130].\n", "- **Schaltverhalten (`shift_behavior`)**: ordinal skaliert mit 3 Kategorien, gezogen aus einer Multinomialverteilung (Wahrscheinlichkeiten: 40% früh, 40% normal, 20% spät).\n", "- **Harte Bremsmanöver (`hard_brakes`)**: metrisch, modelliert mit einer Poisson-Verteilung (λ = 2).\n", "- **Geschwindigkeitsüberschreitungen (`speeding`)**: ordinal skaliert mit 3 Stufen, abhängig von `avg_speed` über eine Softmax-basierte Wahrscheinlichkeitsverteilung.\n", "- **Wetterbedingungen (`weather`)**: nominal skaliert, zufällig verteilt mit 75% trocken, 20% nass und 5% winterlich.\n", "- **Fahrstrecke (`trip_distance`)**: lognormal verteilt mit μ = ln(12) und σ = 0.7.\n", "- **Straßentyp (`road_type`)**: abhängig von `trip_distance`, mit kategorialer Softmax-Verteilung (Innerorts, Außerorts, Autobahn).\n", "- **Wochentag (`weekday`)**: nominal mit festgelegter Verteilung (70% Werktage, je 15% Samstag und Sonntag).\n", "\n", "### Modellierte Abhängigkeiten\n", "\n", "- Die Variablen `speeding` und `road_type` wurden nicht rein zufällig erzeugt, sondern basieren auf skalierter Eingangsvariablen (`avg_speed` bzw. `trip_distance`) und einem gewichteten Softmax-Verfahren. Dies erlaubt realistischere Zusammenhänge.\n", "\n", "### Zielvariable und 𝑝ᵢ-Werte\n", "\n", "Die Zielvariable, die eine Diebstahlwahrscheinlichkeit beschreibt, wurde aus diesen Features modelliert. Die 𝑝ᵢ-Werte repräsentieren dabei individuelle Wahrscheinlichkeiten für Fahrerwechsel und wurden anhand von Beta-Verteilungen erzeugt.\n", "\n", "### Wahl und Begründung der Beta-Werte\n", "\n", "Die Beta-Gewichte wurden bewusst so gewählt, dass sie realistische Zusammenhänge zwischen den Merkmalen und dem Auftreten eines möglichen Diebstahls modellieren, ohne die Zielvariable künstlich zu verzerren. Das Ziel war ein realistischer, aber nicht übermäßig häufiger positiver Klassenanteil.\n", "\n", "- **`avg_speed (0.015)`**: Ein leicht positiver Zusammenhang, da ungewöhnliche Geschwindigkeit (insbesondere zu hohe) auf atypisches Fahrverhalten hindeuten kann.\n", "- **`hard_brakes (0.1)`**: Ein höherer Beta-Wert, da aggressive Bremsmanöver oft stark mit ungewohnten Fahrstilen korrelieren.\n", "- **`trip_distance (0)`**: Kein Einfluss, da die reine Strecke keine Aussagekraft hat.\n", "- **`shift_behavior` (0.05, 0.075)**: Personen, die spät schalten, zeigen eher sportliches oder ineffizientes Verhalten – potenziell abweichend vom üblichen Fahrerprofil.\n", "- **`speeding` (0.05, 0.075)**: Häufige Geschwindigkeitsüberschreitungen deuten ebenfalls auf untypisches Fahrverhalten hin.\n", "- **`weather` (0, 0)**: Wetterbedingungen spielen keine Rolle.\n", "- **`road_type` (0, 0)**: Verschiedene Straßentypen implizieren ebenfalls kein Fahrerprofil\n", "- **`weekday` (0, 0)**: Ebenfalls unbedeutend für den Fahrstil.\n", "\n", "Alle Beta-Werte wurden bewusst reduziert (unterhalb von 0.1), um eine Übergewichtung einzelner Faktoren zu vermeiden und die Zielvariable `Diebstahl` in einem nahe zu realistischen Bereich zu halten. Eine stärkere Gewichtung hätte zu einer zu häufigen Klassifikation als Diebstahl geführt.\n", "\n", "```visualisierung\n", "Die grafische Darstellung zeigt, dass rund 25 % der Fahrten als potenzielle Diebstähle klassifiziert wurden – ein realistischer Wert, der ein geeignetes Trainingsverhältnis für ein Klassifikationsmodell ermöglicht.\n", "```\n", "\n" ] }, { "cell_type": "markdown", "id": "adbc166b", "metadata": {}, "source": [ "## Simulation der Perspektive des Data Scientist" ] }, { "cell_type": "markdown", "id": "f8abd5f5", "metadata": {}, "source": [ "### Stichprobe entnehmen" ] }, { "cell_type": "code", "execution_count": 8, "id": "706ff5c6", "metadata": {}, "outputs": [], "source": [ "sample_size = 20_000\n", "sample_indices = np.random.choice(X.index, size=sample_size, replace=False)\n", "X_sample = X.loc[sample_indices]\n", "target_sample = target[sample_indices]" ] }, { "cell_type": "markdown", "id": "9b67610e", "metadata": {}, "source": [ "### Datenexploration und -vorverarbeitung\n", "\n", "Zunächst verschafft sich der Data Scientist einen Überblick über die Stichprobe, prüft die Verteilung der Zielvariable und der erklärenden Variablen, erkennt potenzielle Ausreißer und prüft auf fehlende Werte. Außerdem werden die Variablentypen für die Modellierung vorbereitet (z.B. One-Hot-Encoding für kategoriale Variablen)." ] }, { "cell_type": "code", "execution_count": 9, "id": "9f12d325", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Form der Stichprobe: (20000, 18)\n", "Anteil Zielvariable (Diebstahl):\n", "0 0.7289\n", "1 0.2711\n", "Name: proportion, dtype: float64\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " avg_speed hard_brakes trip_distance shift_frueh shift_normal \\\n", "count 20000.000000 20000.000000 20000.000000 20000 20000 \n", "unique NaN NaN NaN 2 2 \n", "top NaN NaN NaN False False \n", "freq NaN NaN NaN 12065 11898 \n", "mean 46.955431 1.987300 15.300650 NaN NaN \n", "std 9.983939 1.406321 12.277946 NaN NaN \n", "min 10.000000 0.000000 0.764512 NaN NaN \n", "25% 40.210704 1.000000 7.442576 NaN NaN \n", "50% 46.933676 2.000000 11.922107 NaN NaN \n", "75% 53.726714 3.000000 19.153933 NaN NaN \n", "max 81.665638 10.000000 171.637620 NaN NaN \n", "\n", " shift_spaet speeding_haeufig speeding_manchmal speeding_selten \\\n", "count 20000 20000 20000 20000 \n", "unique 2 2 2 2 \n", "top False False False False \n", "freq 16037 12691 13434 13875 \n", "mean NaN NaN NaN NaN \n", "std NaN NaN NaN NaN \n", "min NaN NaN NaN NaN \n", "25% NaN NaN NaN NaN \n", "50% NaN NaN NaN NaN \n", "75% NaN NaN NaN NaN \n", "max NaN NaN NaN NaN \n", "\n", " weather_nass weather_trocken weather_winterlich road_Autobahn \\\n", "count 20000 20000 20000 20000 \n", "unique 2 2 2 2 \n", "top False True False False \n", "freq 16038 15060 19022 13217 \n", "mean NaN NaN NaN NaN \n", "std NaN NaN NaN NaN \n", "min NaN NaN NaN NaN \n", "25% NaN NaN NaN NaN \n", "50% NaN NaN NaN NaN \n", "75% NaN NaN NaN NaN \n", "max NaN NaN NaN NaN \n", "\n", " road_Außerorts road_Innerorts weekday_Mo-Fr weekday_Sa weekday_So \n", "count 20000 20000 20000 20000 20000 \n", "unique 2 2 2 2 2 \n", "top False False True False False \n", "freq 13305 13478 13992 17041 16951 \n", "mean NaN NaN NaN NaN NaN \n", "std NaN NaN NaN NaN NaN \n", "min NaN NaN NaN NaN NaN \n", "25% NaN NaN NaN NaN NaN \n", "50% NaN NaN NaN NaN NaN \n", "75% NaN NaN NaN NaN NaN \n", "max NaN NaN NaN NaN NaN " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Überblick über die Stichprobe\n", "print(\"Form der Stichprobe:\", X_sample.shape)\n", "print(\"Anteil Zielvariable (Diebstahl):\")\n", "print(pd.Series(target_sample).value_counts(normalize=True))\n", "\n", "# Merkmale\n", "X_sample.describe(include='all')" ] }, { "cell_type": "code", "execution_count": 10, "id": "30bfcdde", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fehlende Werte pro Spalte:\n", "avg_speed 0\n", "hard_brakes 0\n", "trip_distance 0\n", "shift_frueh 0\n", "shift_normal 0\n", "shift_spaet 0\n", "speeding_haeufig 0\n", "speeding_manchmal 0\n", "speeding_selten 0\n", "weather_nass 0\n", "weather_trocken 0\n", "weather_winterlich 0\n", "road_Autobahn 0\n", "road_Außerorts 0\n", "road_Innerorts 0\n", "weekday_Mo-Fr 0\n", "weekday_Sa 0\n", "weekday_So 0\n", "dtype: int64\n", "\n", "avg_speed 0\n", "hard_brakes 0\n", "trip_distance 0\n", "shift_frueh 0\n", "shift_normal 0\n", "shift_spaet 0\n", "speeding_haeufig 0\n", "speeding_manchmal 0\n", "speeding_selten 0\n", "weather_nass 0\n", "weather_trocken 0\n", "weather_winterlich 0\n", "road_Autobahn 0\n", "road_Außerorts 0\n", "road_Innerorts 0\n", "weekday_Mo-Fr 0\n", "weekday_Sa 0\n", "weekday_So 0\n", "dtype: int64\n" ] } ], "source": [ "# Prüfung auf fehlende Werte\n", "print(\"Fehlende Werte pro Spalte:\")\n", "print(X_sample.isnull().sum())" ] }, { "cell_type": "markdown", "id": "201bca58", "metadata": {}, "source": [ "### Feature Engineering\n", "\n", "Für die Modellierung werden kategoriale Variablen in numerische Dummy-Variablen umgewandelt. Dies ist notwendig, damit die Regressionsmodelle die Informationen nutzen können. Außerdem werden die Daten in Trainings- und Testdaten aufgeteilt, um eine objektive Modellbewertung zu ermöglichen. In unserem Fall sind die kategorischen Variablen schon aus dem vorherigen Schritt One-Hot Encoded, weil wir aus den Variablen das logistische Regressionsproblem erstellt haben." ] }, { "cell_type": "code", "execution_count": 11, "id": "b71ab517", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trainingsdaten: (14000, 18)\n", "Testdaten: (6000, 18)\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# Zielvariable\n", "y = target_sample\n", "\n", "# One-Hot-Encoding für kategoriale Variablen\n", "X_encoded = pd.get_dummies(X_sample, drop_first=True)\n", "\n", "# Aufteilung in Trainings- und Testdaten\n", "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.3, random_state=42)\n", "print(\"Trainingsdaten:\", X_train.shape)\n", "print(\"Testdaten:\", X_test.shape)" ] }, { "cell_type": "markdown", "id": "761c0673", "metadata": {}, "source": [ "### Modellierung der Korrelationen und kategorischen Variablen" ] }, { "cell_type": "code", "execution_count": 12, "id": "8a320b4c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Korrelationsmatrix\n", "plt.figure(figsize=(12, 10))\n", "sns.heatmap(X_train.corr(), annot=True, fmt=\".2f\", cmap='coolwarm', square=True, cbar_kws={\"shrink\": .8})\n", "plt.title('Korrelationsmatrix der Merkmale')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "684d6fee", "metadata": {}, "source": [ "Man kann erkennen das die kategorischen Variablen natürlich eine stärkere Korrelation haben, weil sie schon in Dummies umgewandelt wurden. Ansonsten gibt es keine stärkeren Korrelationen, sodass wir sehr wahrscheinlich kein Multikolinearitätsproblem haben." ] }, { "cell_type": "code", "execution_count": 13, "id": "b748cb80", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_15840\\146936503.py:6: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_15840\\146936503.py:6: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_15840\\146936503.py:6: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_15840\\146936503.py:6: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n" ] }, { "data": { "image/png": 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ExERr4JcyBu90D/qduFt5Ly8vXbhwIcP08+fPS/q/fY6IiJCzs7M2bdpkEyg+/fTTDOuT/vrO+/r6WqenHxcP6+bNm/riiy/0zDPPWL9Pt79vt7uf38+ztwdpC5ycnDR48GANHjxYV69e1RdffKERI0aoadOmio6OVq5cuR5n1QGIsAXATlJTU7V48WI988wzmj9/fob5mzZt0pQpU7Rly5YM3XH+yYIFCzRmzBi99957d/2B1PQTyttPViVpzpw5D7Sdf6N+/fqaNGmSli9frv79+1unr1ixwqZcrly5VL9+fR05ckQVK1a0XkF/lGrUqKHy5ctr0aJFKlWqlDw8PGy6IjZs2FCTJk3SsmXLNGjQIOv0tWvXKiEhQQ0bNnyg7b3zzjtyc3PToEGDlJCQoNDQUEmydltbtmyZTYA7ePCgTp48ab0z8iQICAjQ+PHj5eXllSHsPg7pv7V17Ngxm/dq48aNpm2zYcOGCg0N1bfffqtnn33WOn3JkiWyWCyqX7++pL+OKycnJ5uLIzdv3tTSpUtt1levXj1J0vLly2266q1evfquP1fwIFJTU9W3b19duXLF+v2SbN+30qVLW6dv2LDhobb3OPzbtiBv3rxq06aNfv/9dw0cOFBnz55VuXLlTK4tgDsRtgDYxZYtW3T+/HlNnDjRevJ1u/Lly2vmzJlasGDBA4WtvXv3qlevXqpdu7YaN26c4bmmmjVrqkyZMnrmmWc0bNgwGYYhT09Pbdy4Udu2bXvY3fpHTZo00X//+18FBwcrISFB1apV09dff53hhFSSPvjgAz3//POqU6eOevfurWLFiunatWs6ffq0Nm7caPOszL/VtWtXDR48WKdOnVLPnj3l6upqnde4cWM1bdpUb7/9tuLj41W7dm0dO3ZMo0ePVpUqVf7Vjx4PGDBAefLkUY8ePXT9+nVNnz5dpUuXVo8ePTRjxgw5ODioefPmOnv2rEaNGiU/Pz+boGdvAwcO1Nq1a/Xf//5XgwYNUsWKFZWWlqbffvtNW7du1ZAhQ0z9Qe4XXnhBnp6eCgoK0nvvvScnJyeFh4crOjratG0OGjRIS5YsUYsWLfTee++paNGi+uyzz/TRRx+pd+/e1mfCWrRoobCwMHXs2FE9evTQlStXNHny5AwXNcqWLatOnTpp2rRpcnZ2VqNGjXT8+HFNnjxZ7u7u912vixcvat++fTIMQ9euXbP+qPHRo0c1aNAgde/e3Vr2ueeeU+nSpTV06FClpKQoX758Wrdunfbs2fNo3iST3W9b0LJlS5UvX17VqlVTgQIF9Ouvv2ratGkqWrSoSpYsaee9ALInwhYAu1iwYIFy5MhhHYzhTvnz59fLL7+sNWvW6OLFi/e93lOnTiklJUVff/21/P39M8w3DEPOzs7auHGjBgwYoJ49e8rJyUmNGjXSF198oaeeeupf79P9cHBw0IYNGzR48GBNmjRJSUlJql27tjZv3qwyZcrYlC1Xrpy+/fZbjR07Vu+8844uXbqkvHnzqmTJktZnNR5WYGCghg0bpqSkJOtvKqWzWCz69NNPFRISokWLFmncuHHKnz+/AgMDNX78+Awn0fcrKChIuXPnVmBgoBISEjR//nzNmjVLzzzzjBYsWKAPP/xQHh4eatasmUJDQx/ZczyPQu7cufXVV19pwoQJmjt3rs6cOSNXV1c99dRTatSokfUOilnc3d0VGRmpgQMHqlOnTsqbN6+6deum5s2bq1u3bqZss0CBAvrmm280fPhwDR8+XPHx8Xr66ac1adIkDR482FquQYMGWrhwoSZOnKiWLVvK19dX3bt3V8GCBa2/7ZZuwYIF8vb2Vnh4uKZPn67KlStr7dq11oFy7kd692MHBwflyZNHRYsWlb+/v2bPnq2aNWvalHV0dNTGjRvVt29f9erVSy4uLmrfvr1mzpxp/TH1J9n9tgX169fX2rVrNX/+fMXHx8vHx0eNGzfWqFGj5OzsbMc9ALIvi/GwQ/YAAAAAADJgNEIAAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATMDvbN2ntLQ0nT9/Xm5ubrJYLPauDgAAAAA7Sf9B9cKFC8vB4d73rwhb9+n8+fPy8/OzdzUAAAAAPCGio6NVpEiRe84nbN0nNzc3SX+9oe7u7nauDQAAAAB7iY+Pl5+fnzUj3Ath6z6ldx10d3cnbAEAAAD4x8eLGCADAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABE72rgAyatLuPXtXAch0tq56195VeKQqvx9i7yoAmVLUOyH2rgIAWHFnCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABM4GTvCgAAACCjftsH2LsKQKY0o+EH9q6CFXe2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAEzwxYSs0NFQWi0UDBw60TjMMQyEhISpcuLBcXV1Vr149nThxwma5xMRE9evXT/nz51fu3LnVqlUrnTt3zqZMbGysAgMD5eHhIQ8PDwUGBurq1auPYa8AAAAAZFdPRNg6ePCg5s6dq4oVK9pMnzRpksLCwjRz5kwdPHhQPj4+aty4sa5du2YtM3DgQK1bt04RERHas2ePrl+/roCAAKWmplrLdOzYUVFRUYqMjFRkZKSioqIUGBj42PYPAAAAQPZj97B1/fp1vfbaa5o3b57y5ctnnW4YhqZNm6aRI0eqdevWKl++vBYvXqwbN25oxYoVkqS4uDgtWLBAU6ZMUaNGjVSlShUtW7ZM3333nb744gtJ0smTJxUZGan58+fL399f/v7+mjdvnjZt2qRTp07ZZZ8BAAAAZH12D1tvvvmmWrRooUaNGtlMP3PmjGJiYtSkSRPrNBcXF9WtW1fffPONJOnw4cNKTk62KVO4cGGVL1/eWmbv3r3y8PBQjRo1rGVq1qwpDw8Pa5m7SUxMVHx8vM0fAAAAANwvJ3tuPCIiQt9++60OHjyYYV5MTIwkydvb22a6t7e3fv31V2uZHDly2NwRSy+TvnxMTIwKFiyYYf0FCxa0lrmb0NBQjRkz5sF2CAAAAAD+P7vd2YqOjtaAAQO0bNky5cyZ857lLBaLzWvDMDJMu9OdZe5W/p/WM3z4cMXFxVn/oqOj/3abAAAAAHA7u4Wtw4cP69KlS6pataqcnJzk5OSk3bt3a/r06XJycrLe0brz7tOlS5es83x8fJSUlKTY2Ni/LXPx4sUM2798+XKGu2a3c3Fxkbu7u80fAAAAANwvu4Wthg0b6rvvvlNUVJT1r1q1anrttdcUFRWlp59+Wj4+Ptq2bZt1maSkJO3evVu1atWSJFWtWlXOzs42ZS5cuKDjx49by/j7+ysuLk4HDhywltm/f7/i4uKsZQAAAADgUbPbM1tubm4qX768zbTcuXPLy8vLOn3gwIEaP368SpYsqZIlS2r8+PHKlSuXOnbsKEny8PBQUFCQhgwZIi8vL3l6emro0KGqUKGCdcCNsmXLqlmzZurevbvmzJkjSerRo4cCAgJUunTpx7jHAAAAALITuw6Q8U+Cg4N18+ZN9enTR7GxsapRo4a2bt0qNzc3a5mpU6fKyclJbdu21c2bN9WwYUOFh4fL0dHRWmb58uXq37+/ddTCVq1aaebMmY99fwAAAABkH09U2Nq1a5fNa4vFopCQEIWEhNxzmZw5c2rGjBmaMWPGPct4enpq2bJlj6iWAAAAAPDP7P47WwAAAACQFRG2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQtAAAAADABYQsAAAAATEDYAgAAAAATELYAAAAAwASELQAAAAAwAWELAAAAAExA2AIAAAAAExC2AAAAAMAEhC0AAAAAMAFhCwAAAABMQNgCAAAAABMQtgAAAADABIQ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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_15840\\146936503.py:6: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_dummy_counts(df, prefix):\n", " dummy_cols = [col for col in df.columns if col.startswith(prefix)]\n", " counts = df[dummy_cols].sum().sort_values(ascending=False)\n", " \n", " plt.figure(figsize=(10, 6))\n", " sns.barplot(x=counts.index, y=counts.values, palette='viridis')\n", " plt.title(f'Anzahl der Vorkommen für {prefix} Dummies')\n", " plt.xlabel('Dummy Variablen')\n", " plt.ylabel('Anzahl')\n", " plt.xticks(rotation=45)\n", " plt.show()\n", " \n", "plot_dummy_counts(X_train, 'shift')\n", "plot_dummy_counts(X_train, 'speeding')\n", "plot_dummy_counts(X_train, 'weather')\n", "plot_dummy_counts(X_train, 'road')\n", "plot_dummy_counts(X_train, 'weekday')" ] }, { "cell_type": "markdown", "id": "2127272a", "metadata": {}, "source": [ "### Schrittweise Variablenselektion: Backward Selection\n", "\n", "Um herauszufinden, welche Variablen tatsächlich erklärend für die Zielvariable sind, wird eine schrittweise Rückwärtsselektion (Backward Selection) durchgeführt. Dabei startet man mit allen potenziellen Prädiktoren und entfernt schrittweise die am wenigsten signifikanten Variablen, bis nur noch signifikante Prädiktoren übrig bleiben. Dies hilft, ein einfaches und interpretierbares Modell zu erhalten. Da die Zielvariable binär ist, wird eine logistische Regression verwendet. Das Modell wird auf den Trainingsdaten trainiert und die Modellgüte auf den Testdaten evaluiert." ] }, { "cell_type": "code", "execution_count": 14, "id": "19c7e28c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Entfernte Variablen und zugehörige p-Werte:\n", "weekday_Mo-Fr: p=1.0000\n", "trip_distance: p=0.7961\n", "road_Autobahn: p=1.0000\n", "road_Innerorts: p=0.7788\n", "speeding_haeufig: p=1.0000\n", "weekday_Sa: p=0.6861\n", "weather_winterlich: p=1.0000\n", "shift_normal: p=1.0000\n", "shift_frueh: p=0.9369\n", "shift_spaet: p=0.7977\n", "road_Außerorts: p=0.5723\n", "speeding_selten: p=0.4000\n", "weekday_So: p=0.2843\n", "\n", "Verbleibende Variablen im Modell:\n", "['avg_speed', 'hard_brakes', 'speeding_manchmal', 'weather_nass', 'weather_trocken']\n", "\n", "Zusammenfassung des finalen Modells:\n", " Logit Regression Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 14000\n", "Model: Logit Df Residuals: 13994\n", "Method: MLE Df Model: 5\n", "Date: Thu, 12 Jun 2025 Pseudo R-squ.: 0.006879\n", "Time: 14:40:25 Log-Likelihood: -8108.5\n", "converged: True LL-Null: -8164.6\n", "Covariance Type: nonrobust LLR p-value: 1.314e-22\n", "=====================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-------------------------------------------------------------------------------------\n", "const -1.6939 0.127 -13.365 0.000 -1.942 -1.445\n", "avg_speed 0.0157 0.002 8.231 0.000 0.012 0.019\n", "hard_brakes 0.0781 0.013 5.794 0.000 0.052 0.105\n", "speeding_manchmal -0.0845 0.041 -2.059 0.040 -0.165 -0.004\n", "weather_nass -0.2496 0.093 -2.670 0.008 -0.433 -0.066\n", "weather_trocken -0.1701 0.086 -1.987 0.047 -0.338 -0.002\n", "=====================================================================================\n" ] } ], "source": [ "import statsmodels.api as sm\n", "\n", "def backward_selection(X, y, significance_level=0.05):\n", " X_ = X.copy().astype(float)\n", " removed_features = []\n", " while True:\n", " X_with_const = sm.add_constant(X_)\n", " model = sm.Logit(y, X_with_const).fit(disp=0)\n", " pvalues = model.pvalues.drop('const')\n", " max_pval = pvalues.max()\n", " if max_pval > significance_level:\n", " excluded_feature = pvalues.idxmax()\n", " removed_features.append((excluded_feature, max_pval))\n", " X_ = X_.drop(columns=[excluded_feature])\n", " else:\n", " break\n", " return X_, removed_features, model\n", "\n", "X_selected, removed_features, final_model = backward_selection(X_train, y_train)\n", "\n", "print(\"Entfernte Variablen und zugehörige p-Werte:\")\n", "for feat, pval in removed_features:\n", " print(f\"{feat}: p={pval:.4f}\")\n", "\n", "print(\"\\nVerbleibende Variablen im Modell:\")\n", "print(list(X_selected.columns))\n", "\n", "print(\"\\nZusammenfassung des finalen Modells:\")\n", "print(final_model.summary())" ] }, { "cell_type": "markdown", "id": "257e622b", "metadata": {}, "source": [ "### Interpretation der Modellkoeffizienten\n", "\n", "Die geschätzten Koeffizienten der logistischen Regression geben Aufschluss darüber, welche Variablen einen Einfluss auf die Wahrscheinlichkeit eines Diebstahls haben. Diese werden mit den tatsächlichen, zur Generierung verwendeten Koeffizienten verglichen." ] }, { "cell_type": "code", "execution_count": 15, "id": "6641f80c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Modellkoeffizienten:\n", "const -1.693856\n", "avg_speed 0.015739\n", "hard_brakes 0.078115\n", "speeding_manchmal -0.084460\n", "weather_nass -0.249625\n", "weather_trocken -0.170069\n", "dtype: float64\n" ] } ], "source": [ "# Modellkoeffizienten anzeigen\n", "coefficients = final_model.params\n", "print(\"\\nModellkoeffizienten:\")\n", "print(coefficients)" ] }, { "cell_type": "markdown", "id": "4194adef", "metadata": {}, "source": [ "### Vergleich mit dem Generierungsmodell (Verlassen der Anwenderperspektive)\n", "\n", "Abschließend wird das trainierte Modell mit dem tatsächlich zur Generierung der Zielvariable verwendeten Modell verglichen. Die geschätzten Koeffizienten sollten – abgesehen von Zufallsschwankungen – mit den wahren Koeffizienten übereinstimmen. Dies zeigt, dass das systematische Vorgehen des Data Scientist erfolgreich war und das zugrundeliegende Modell korrekt rekonstruiert wurde." ] }, { "cell_type": "code", "execution_count": 16, "id": "0541fa09", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vergleich der geschätzten und wahren Koeffizienten:\n", " Geschätzte Koeffizienten Wahre Koeffizienten\n", "avg_speed 0.015739 0.015\n", "const -1.693856 NaN\n", "hard_brakes 0.078115 0.100\n", "road_Autobahn NaN 0.000\n", "shift_frueh NaN 0.050\n", "shift_normal NaN 0.075\n", "shift_spaet NaN 0.050\n", "speeding_haeufig NaN 0.075\n", "speeding_manchmal -0.084460 0.000\n", "speeding_selten NaN 0.000\n", "trip_distance NaN 0.000\n", "weather_nass -0.249625 0.000\n", "weather_trocken -0.170069 0.000\n", "weather_winterlich NaN 0.000\n" ] } ], "source": [ "# Tatsächliche Betas aus der Generierung (siehe oben)\n", "true_betas = pd.Series([\n", " 0.015, # avg_speed\n", " 0.1, # hard_brakes\n", " 0., # trip_distance\n", " 0.05, 0.075, # shift_dummies\n", " 0.05, 0.075, # speeding_dummies\n", " 0., 0., # weather_dummies\n", " 0., 0., # road_dummies\n", " 0., 0. # weekday_dummies\n", "], index=X_train.columns[:13]) # Die Reihenfolge entspricht der Generierung\n", "\n", "print(\"Vergleich der geschätzten und wahren Koeffizienten:\")\n", "comparison = pd.DataFrame({\n", " 'Geschätzte Koeffizienten': coefficients,\n", " 'Wahre Koeffizienten': true_betas\n", "})\n", "print(comparison)" ] }, { "cell_type": "markdown", "id": "7516554c", "metadata": {}, "source": [ "## Güte der Modellparameter" ] }, { "cell_type": "markdown", "id": "18f9a07d", "metadata": {}, "source": [] }, { "cell_type": "markdown", "id": "c1bb79ce", "metadata": {}, "source": [ "### Güte der Modellparameter: Einfluss des Stichprobenumfangs\n", "\n", "In diesem Abschnitt wird untersucht, wie sich der Stichprobenumfang auf die Schätzgüte der Modellparameter auswirkt. Dazu werden jeweils k = 1.000 zufällige Stichproben aus der Grundgesamtheit gezogen, mit Stichprobenumfängen n von 1.000 bis 50.000 (Schrittweite 5.000). Für jede Stichprobe wird das optimale Modell aus Abschnitt 3 trainiert und der geschätzte Beta-Wert einer erklärenden Variable (z.B. `avg_speed`) gespeichert. Die Verteilungen der geschätzten Beta-Werte werden für drei ausgewählte Stichprobenumfänge verglichen und der funktionale Zusammenhang zwischen n und der Standardabweichung der Schätzungen analysiert." ] }, { "cell_type": "code", "execution_count": 17, "id": "616ae484", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Stichprobenumfang: 0%| | 0/10 [00:00 3\u001b[0m sns\u001b[38;5;241m.\u001b[39mkdeplot(betas_by_n[n][\u001b[38;5;241m~\u001b[39mnp\u001b[38;5;241m.\u001b[39misnan(betas_by_n[n])], label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVerteilung des geschätzten Beta-Werts für avg_speed bei verschiedenen Stichprobenumfängen\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 5\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGeschätzter Beta-Wert für avg_speed\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "\u001b[1;31mKeyError\u001b[0m: 10000" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 6))\n", "for n in [1000, 10000, 50000]:\n", " sns.kdeplot(betas_by_n[n][~np.isnan(betas_by_n[n])], label=f\"n={n}\")\n", "plt.title(\"Verteilung des geschätzten Beta-Werts für avg_speed bei verschiedenen Stichprobenumfängen\")\n", "plt.xlabel(\"Geschätzter Beta-Wert für avg_speed\")\n", "plt.ylabel(\"Dichte\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "990bb2a0", "metadata": {}, "source": [ "**Interpretation:**\n", "\n", "Mit wachsendem Stichprobenumfang werden die Verteilungen der geschätzten Beta-Werte schmaler und konzentrieren sich stärker um den wahren Wert. Kleine Stichproben führen zu einer größeren Streuung und damit zu unsichereren Schätzungen." ] }, { "cell_type": "markdown", "id": "50ea2928", "metadata": {}, "source": [ "### Funktionaler Zusammenhang zwischen n und der Standardabweichung des geschätzten Beta-Werts\n", "\n", "Für jeden Stichprobenumfang wird die Standardabweichung der geschätzten Beta-Werte berechnet und gegen n aufgetragen. Zusätzlich wird die theoretische Entwicklung der Standardabweichung nach der Formel $\\sigma(\\hat{\\beta}) \\propto 1/\\sqrt{n}$ eingezeichnet." ] }, { "cell_type": "code", "execution_count": null, "id": "cf7a7e61", "metadata": {}, "outputs": [], "source": [ "stds = np.array([np.nanstd(betas_by_n[n]) for n in n_values])\n", "\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(n_values, stds, marker='o', label='Empirische Standardabweichung')\n", "plt.plot(n_values, stds[0]*np.sqrt(n_values[0]/n_values), '--', label=r'$\\propto 1/\\sqrt{n}$')\n", "plt.xlabel('Stichprobenumfang n')\n", "plt.ylabel('Standardabweichung des geschätzten Beta-Werts')\n", "plt.title('Zusammenhang zwischen Stichprobenumfang und Schätzgenauigkeit')\n", "plt.legend()\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "10afcd69", "metadata": {}, "source": [ "**Fazit:**\n", "\n", "Die Standardabweichung der Schätzung eines Modellparameters nimmt mit wachsendem Stichprobenumfang näherungsweise proportional zu $1/\\sqrt{n}$ ab. Das bedeutet: Je mehr Daten zur Verfügung stehen, desto präziser und stabiler werden die Parameterschätzungen. Die Lernqualität eines Modells steigt also mit der Datenmenge, wobei der Zugewinn mit zunehmender Datenmenge abnimmt (abnehmender Grenznutzen)." ] } ], "metadata": { "kernelspec": { "display_name": "hft_ml", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }