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DSAI/05_ML/code/supervised_3_svm.ipynb
2025-05-01 19:31:40 +02:00

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{
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"cell_type": "markdown",
"metadata": {},
"source": [
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"\n",
"<h1 style=\"margin: 0; color: #4CAF50;\">Supervised ML Modelle: Support-Vector-Machines</h1>\n",
"<h2 style=\"margin: 5px 0; color: #555;\">DSAI</h2>\n",
"<h3 style=\"margin: 5px 0; color: #555;\">Jakob Eggl</h3>\n",
"\n",
"<div style=\"flex-shrink: 0;\">\n",
" <img src=\"https://www.htl-grieskirchen.at/wp/wp-content/uploads/2022/11/logo_bildschirm-1024x503.png\" alt=\"Logo\" style=\"width: 250px; height: auto;\"/>\n",
"</div>\n",
"<p1> © 2024/25 Jakob Eggl. Nutzung oder Verbreitung nur mit ausdrücklicher Genehmigung des Autors.</p1>\n",
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"<div style=\"flex: 1;\">\n",
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{
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"source": [
"Teile (adaptiert) von *AI Inside Seminar KI Mag. Otto Reichel*!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Support Vector Machines (SVM's)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nun betrachten wir den Support Vector Machine (SVM) Klassifizierer. Es ist eine der am meisten theoretisch fundierten Machine Learning Modelle.\n",
"\n",
"Die SVM $\\ldots$\n",
"* $\\ldots$ wird bei uns verwendet zur Klassifizierung\n",
"* $\\ldots$ gibt es in 2 Varianten: **Linear SVM** und **Kernel SVM**\n",
"* $\\ldots$ hat wenige Hyperparameter ($C$ + Parameter für Kernel SVM)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![SVM_Preview_3_Classes](../resources/SVM_Preview.jpg)\n",
"\n",
"(von https://spotintelligence.com/2024/05/06/support-vector-machines-svm/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Beschreibung des Algorithmus"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SVM's sind Supervised Machine Learning Modelle, die versuchen, Muster in Daten zu erkennen, um die Daten zu klassifizieren. Ähnlich wie beim $k$-Nearest-Neighbour Klassifizierer, erhalten wir auch hier keine Wahrscheinlichkeiten zu bestimmten Klassenzugehörigkeiten, sondern nur die Vorhersage, ob ein bestimmter Datensatz zu einer Klasse gehört oder nicht.\n",
"\n",
"Dabei versuchen SVM's Trennlinien (im mehrdimensionalen Hyperebenen) zwischen den Bereichen so zu legen, dass der leere Bereich (=Streifen, genannt **Margin**) möglichst groß wird. Die folgende Abbildung zeigt dies für 2 klar getrennte Bereiche. In der Praxis ist die Ausgangslage meistens nicht so eindeutig, sprich hier ist es meistens *nicht* möglich, ohne Fehler eine Trennlinie/Hyperebene zu finden."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![SVM_Margin_Visualization](../resources/SVM_Margin.png)\n",
"\n",
"(von https://www.iunera.com/kraken/fabric/support-vector-machines-svm/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Welche Hyperplane wäre hier die beste?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![SVM_Multiple_Hyperplanes](../resources/SVM_Multiple_Hyperplanes.jpg)\n",
"\n",
"![SVM_Wrong_Hyperplanes](../resources/SVM_Wrong_Hyperplane.jpg)\n",
"\n",
"![SVM_Right_Hyperplanes](../resources/SVM_Right_Hyperplane.jpg)\n",
"\n",
"(von VL: Supervised Machine Learning JKU WS 2022)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Da der Trennbereich so groß wie möglich sein soll, ist klarerweise die letzte Hyperplane die beste."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training vs. Inferenz von SVM's"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wir wollen hier nochmal formal festhalten, wie eine Support Vector Machine an den Daten angepasst wird (Training) und wie sie dann verwendet wird.\n",
"\n",
"**Training:**\n",
"* Die optimale Hyperebene wird berechnet.\n",
"* Je nach Parameter $C$ werden mehr oder weniger Fehlklassifikationen erlaubt\n",
"\n",
"**Verwendung (=Inferenz):**\n",
"* Ein neuer Datenpunkt wird in den Raum gesetzt\n",
"* Je nachdem, auf welcher Seite der Punkt liegt, bekommt er das Label $1$ oder $0$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiclass SVM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wie gehen wir vor, wenn wir mehrere Klassen haben?\n",
"\n",
"Hier gibt es *mehrere Möglichkeiten*.\n",
"\n",
"1. **One-vs-One (OvO)**\n",
" * Für $N$ Klassen wird für jede mögliche Paarung von Klassen eine eigene SVM trainiert\n",
" * Das bedeutet, es werden $N\\cdot (N-1)/2$ Klassifikatoren trainiert\n",
" * Vorhersage: Jeder Klassifikator gibt eine Stimme für eine der beiden Klassen ab, und die Klasse mit den meisten Stimmen gewinnt (Mehrheitsentscheidung)\n",
" * Vorteil: Hohe Genauigkeit\n",
" * Nachteil: Teuer (Rechenleistung) bei vielen Klassen\n",
"\n",
"2. **One-vs-All (OvA)**\n",
" * Für $N$ Klassen werden $N$ SVMs trainiert\n",
" * Jede SVM unterscheidet eine Klasse von allen anderen Klassen (z. B. \"Ist es Klasse A oder nicht?\")\n",
" * Vorhersage: Die SVM mit der höchsten Entscheidungsfunktion bestimmt die Klasse\n",
" * Vorteil: Weniger Modelle als *OvO*\n",
" * Nachteil: Schlechtere Performance bei unausgeglichenen Daten"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die Support Vektoren"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nachdem dieses Machine Learning Modell *Support Vector Machine* heißt, ist diese Namensgebung zu hinterfragen sehr legitim. Der Name kommt von den sogenannten **Support Vektoren**.\n",
"\n",
"**Support Vektoren** (deutsch: Stützvektoren) sind die Datenpunkte, die am nächsten zur anderen Klasse sind. Diese werden verwendet, um die optimale Hyperebene zu finden. Die Berechnung der Hyperebene werden wir nicht händisch durchmachen, es kann bequem in Python gemacht werden. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Der Hyperparameter $C$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Wird **Regularisierungsparameter** genannt\n",
"* Kontrolliert den Trade-off zwischen **Maximierung der Margin** und **Minimierung von Fehlklassifikationen**\n",
"* Höheres $C$: Strengere Klassifikation, weniger Fehlklassifikationen erlaubt und somit kleinere Margin\n",
"* Niedrigeres $C$: Größere Margin, erlaubt mehr Fehlklassifikationen für bessere Generalisierung"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
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"text/plain": [
"<Figure size 1500x1000 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.svm import SVC\n",
"from sklearn.datasets import make_classification\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Dummy Dataset erstellen\n",
"X, y = make_classification(\n",
" n_samples=200, \n",
" n_features=2, # Gesamtzahl der Features\n",
" n_informative=2, \n",
" n_redundant=0, # Keine redundanten Features\n",
" n_repeated=0, # Keine wiederholten Features\n",
" n_classes=2, \n",
" n_clusters_per_class=1, \n",
" random_state=42\n",
")\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"scaler = StandardScaler()\n",
"X_train = scaler.fit_transform(X_train)\n",
"X_test = scaler.transform(X_test)\n",
"\n",
"# Verschiedene C-Werte testen\n",
"C_values = [0.01, 0.1, 1, 10, 100]\n",
"plt.figure(figsize=(15, 10))\n",
"\n",
"for i, C in enumerate(C_values, 1):\n",
" model = SVC(C=C, kernel='linear')\n",
" model.fit(X_train, y_train)\n",
" \n",
" # Plot der Entscheidungsgrenze\n",
" plt.subplot(2, 3, i)\n",
" plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap='coolwarm', edgecolors='k')\n",
" \n",
" # Grenzen für die Darstellung\n",
" ax = plt.gca()\n",
" xlim = ax.get_xlim()\n",
" ylim = ax.get_ylim()\n",
" \n",
" xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 100),\n",
" np.linspace(ylim[0], ylim[1], 100))\n",
" Z = model.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
" Z = Z.reshape(xx.shape)\n",
" \n",
" plt.contour(xx, yy, Z, levels=[-1, 0, 1], linestyles=['dashed', 'solid', 'dashed'], colors='black')\n",
" plt.title(f'C = {C}')\n",
" \n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Welche Werte von $C$ begünstigen *Overfitting* und welche *Underfitting*?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Kernel SVM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Als Motivation wird nun folgendes Szenario präsentiert."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Annahme wir wollen folgende 2 Klassen mit Hilfe von einer Linie separieren."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![SVM_Motivation_1d](../resources/Kernel_SVM_Motivation.jpg)\n",
"\n",
"(von https://medium.com/towards-data-science/svm-and-kernel-svm-fed02bef1200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dies ist nicht möglich. Jedoch wäre es einfach, wenn wir auch nicht-lineare Separierungen zulassen würden. Dies ist equivalent zu folgendem."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![SVM_Motivation_1d](../resources/Kernel_SVM_Motivation_Solution.jpg)\n",
"\n",
"(von https://medium.com/towards-data-science/svm-and-kernel-svm-fed02bef1200)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dies bringt uns zu den sogenannten **Kernel SVM's**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Da für komplizierte Datasets das finden einer *Ebene* nahezu unmöglich ist, gibt es eine Erweiterung zu den klassischen Support Vector Machines. Diese werden **kernel SVM's** genannt. Dabei werden die Datenpunkte mit Hilfe einer *kernel-Funktion* in einen höherdimensionalen Raum gebracht. Anschließend wird eine Hyperebene gesucht, welche die Daten separiert. Dieser Vorgang ist in den folgenden Bildern visualisiert."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Kernel_SVM_Preview](../resources/Kernel_SVM_Preview.jpg)\n",
"\n",
"![Kernel_SVM_Preview_2](../resources/Kernel_SVM_Preview2.jpg)\n",
"\n",
"(beide von https://medium.com/@abhishekjainindore24/svm-kernels-and-its-type-dfc3d5f2dcd8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Wichtig:** Mit *Kernel SVM* schaffen wir eine nichtlineare Separierung der Daten im ursprünglichen Raum. Der Umweg über einen höher-dimensionalen Raum, wo wir linear separieren können, beschreibt nur den Vorgang, der im Hintergrund passiert. Insgesamt sind wir trotzdem noch in eine (in diesem Fall *nichtlineare*) Separierung der Daten interessiert."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Info:** Die Funktionsweise der *Kernel-SVM's* passiert mit dem sogenannten **Kernel-Trick**. Dieser erlaubt uns die effiziente Separierung auch in hohen Dimensionen am Computer. Dies wird hier nicht genauer erläutert."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Die verschiedenen Kernels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Linearer Kernel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\\begin{equation*}\n",
" \\phi(x,y)= x\\cdot y\n",
"\\end{equation*}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![SVM_Linear_Kernel](../resources/SVM_linear_kernel.jpg)\n",
"\n",
"(von *Mathematical Pictures at a Data Science Exhibition*)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Polynomial Kernel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\\begin{equation*}\n",
"\t\t\\phi(x, y) := \\left(x\\cdot y +\\beta\\right)^\\alpha,\n",
"\\end{equation*}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![SVM_Polynomial_Kernel](../resources/SVM_Polynomial_Kernel3.png)\n",
"\n",
"(von *Mathematical Pictures at a Data Science Exhibition*)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### RBF Kernel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\\begin{equation*}\n",
" \\phi(x,y) = \\exp\\left(-\\frac{1}{2\\sigma^2}\\vert x-y\\vert^2\\right)\n",
"\\end{equation*}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![SVM_RBF_Kernel](../resources/SVM_Gaussian_Kernel.png)\n",
"\n",
"(von *Mathematical Pictures at a Data Science Exhibition*)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sigmoid Kernel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\\begin{equation*}\n",
" \\phi(x,y) = \\tanh(\\alpha (x\\cdot y) + \\beta)\n",
"\\end{equation*}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![SVM_Sigmoid_Kernel](../resources/SVM_sigmoid_kernel.jpg)\n",
"\n",
"(von [Link](https://www.linkedin.com/pulse/support-vector-machine-explained-theory-visualization-zixuan-zhang/))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Vorteile und Nachteile von (Kernel) SVM's"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Vorteile:**\n",
"* Gute theoretische Fundierung (Eingeschränkte Optimierung)\n",
"* Schnelles Training und Testing\n",
"* Gute Performance bei hoher Anzahl an Features und nur begrenzter Anzahl an Datenpunkten\n",
"\n",
"**Nachteile:**\n",
"* Schlecht, wenn Daten mit viel Rauschen überlagert sind\n",
"* Wenig geeignet für große Datasets\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tipps und Tricks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Normalisierung (zBsp. $z$-score Normalisierung) der Daten auch hier erforderlich\n",
"* Wähle den richtigen Kernel!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (Kernel) SVM's in Python"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import datasets\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.svm import SVC\n",
"from sklearn.metrics import accuracy_score, classification_report"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"iris = datasets.load_iris()\n",
"X = iris.data[:, :2] # Wir verwenden nur die ersten zwei Merkmale für bessere Visualisierung\n",
"y = iris.target\n",
"\n",
"# Aufteilen der Daten in Trainings- und Testset (80% Training, 20% Test)\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[5.1, 3.5],\n",
" [4.9, 3. ],\n",
" [4.7, 3.2],\n",
" [4.6, 3.1],\n",
" [5. , 3.6],\n",
" [5.4, 3.9],\n",
" [4.6, 3.4],\n",
" [5. , 3.4],\n",
" [4.4, 2.9],\n",
" [4.9, 3.1],\n",
" [5.4, 3.7],\n",
" [4.8, 3.4],\n",
" [4.8, 3. ],\n",
" [4.3, 3. ],\n",
" [5.8, 4. ],\n",
" [5.7, 4.4],\n",
" [5.4, 3.9],\n",
" [5.1, 3.5],\n",
" [5.7, 3.8],\n",
" [5.1, 3.8],\n",
" [5.4, 3.4],\n",
" [5.1, 3.7],\n",
" [4.6, 3.6],\n",
" [5.1, 3.3],\n",
" [4.8, 3.4],\n",
" [5. , 3. ],\n",
" [5. , 3.4],\n",
" [5.2, 3.5],\n",
" [5.2, 3.4],\n",
" [4.7, 3.2],\n",
" [4.8, 3.1],\n",
" [5.4, 3.4],\n",
" [5.2, 4.1],\n",
" [5.5, 4.2],\n",
" [4.9, 3.1],\n",
" [5. , 3.2],\n",
" [5.5, 3.5],\n",
" [4.9, 3.6],\n",
" [4.4, 3. ],\n",
" [5.1, 3.4],\n",
" [5. , 3.5],\n",
" [4.5, 2.3],\n",
" [4.4, 3.2],\n",
" [5. , 3.5],\n",
" [5.1, 3.8],\n",
" [4.8, 3. ],\n",
" [5.1, 3.8],\n",
" [4.6, 3.2],\n",
" [5.3, 3.7],\n",
" [5. , 3.3],\n",
" [7. , 3.2],\n",
" [6.4, 3.2],\n",
" [6.9, 3.1],\n",
" [5.5, 2.3],\n",
" [6.5, 2.8],\n",
" [5.7, 2.8],\n",
" [6.3, 3.3],\n",
" [4.9, 2.4],\n",
" [6.6, 2.9],\n",
" [5.2, 2.7],\n",
" [5. , 2. ],\n",
" [5.9, 3. ],\n",
" [6. , 2.2],\n",
" [6.1, 2.9],\n",
" [5.6, 2.9],\n",
" [6.7, 3.1],\n",
" [5.6, 3. ],\n",
" [5.8, 2.7],\n",
" [6.2, 2.2],\n",
" [5.6, 2.5],\n",
" [5.9, 3.2],\n",
" [6.1, 2.8],\n",
" [6.3, 2.5],\n",
" [6.1, 2.8],\n",
" [6.4, 2.9],\n",
" [6.6, 3. ],\n",
" [6.8, 2.8],\n",
" [6.7, 3. ],\n",
" [6. , 2.9],\n",
" [5.7, 2.6],\n",
" [5.5, 2.4],\n",
" [5.5, 2.4],\n",
" [5.8, 2.7],\n",
" [6. , 2.7],\n",
" [5.4, 3. ],\n",
" [6. , 3.4],\n",
" [6.7, 3.1],\n",
" [6.3, 2.3],\n",
" [5.6, 3. ],\n",
" [5.5, 2.5],\n",
" [5.5, 2.6],\n",
" [6.1, 3. ],\n",
" [5.8, 2.6],\n",
" [5. , 2.3],\n",
" [5.6, 2.7],\n",
" [5.7, 3. ],\n",
" [5.7, 2.9],\n",
" [6.2, 2.9],\n",
" [5.1, 2.5],\n",
" [5.7, 2.8],\n",
" [6.3, 3.3],\n",
" [5.8, 2.7],\n",
" [7.1, 3. ],\n",
" [6.3, 2.9],\n",
" [6.5, 3. ],\n",
" [7.6, 3. ],\n",
" [4.9, 2.5],\n",
" [7.3, 2.9],\n",
" [6.7, 2.5],\n",
" [7.2, 3.6],\n",
" [6.5, 3.2],\n",
" [6.4, 2.7],\n",
" [6.8, 3. ],\n",
" [5.7, 2.5],\n",
" [5.8, 2.8],\n",
" [6.4, 3.2],\n",
" [6.5, 3. ],\n",
" [7.7, 3.8],\n",
" [7.7, 2.6],\n",
" [6. , 2.2],\n",
" [6.9, 3.2],\n",
" [5.6, 2.8],\n",
" [7.7, 2.8],\n",
" [6.3, 2.7],\n",
" [6.7, 3.3],\n",
" [7.2, 3.2],\n",
" [6.2, 2.8],\n",
" [6.1, 3. ],\n",
" [6.4, 2.8],\n",
" [7.2, 3. ],\n",
" [7.4, 2.8],\n",
" [7.9, 3.8],\n",
" [6.4, 2.8],\n",
" [6.3, 2.8],\n",
" [6.1, 2.6],\n",
" [7.7, 3. ],\n",
" [6.3, 3.4],\n",
" [6.4, 3.1],\n",
" [6. , 3. ],\n",
" [6.9, 3.1],\n",
" [6.7, 3.1],\n",
" [6.9, 3.1],\n",
" [5.8, 2.7],\n",
" [6.8, 3.2],\n",
" [6.7, 3.3],\n",
" [6.7, 3. ],\n",
" [6.3, 2.5],\n",
" [6.5, 3. ],\n",
" [6.2, 3.4],\n",
" [5.9, 3. ]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(150, 2)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# SVM-Modell mit RBF-Kernel\n",
"svm_model = SVC(kernel='rbf', gamma='scale', C=1.0)\n",
"svm_model = svm_model.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9\n"
]
}
],
"source": [
"# Vorhersagen auf dem Testset\n",
"y_pred = svm_model.predict(X_test)\n",
"\n",
"# Evaluierung der Genauigkeit\n",
"print(\"Accuracy:\", accuracy_score(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def plot_decision_boundary(X, y, model):\n",
" h = .02 # Schrittweite für das Meshgrid\n",
" x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
" y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
"\n",
" Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
" Z = Z.reshape(xx.shape)\n",
"\n",
" plt.contourf(xx, yy, Z, alpha=0.3)\n",
" plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o')\n",
" plt.xlabel('Feature 1')\n",
" plt.ylabel('Feature 2')\n",
" plt.title('SVM Decision Boundary')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_decision_boundary(X_train, y_train, svm_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Was passiert bei einem linearen Kernel?"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {\n",
" /* Definition of color scheme common for light and dark mode */\n",
" --sklearn-color-text: black;\n",
" --sklearn-color-line: gray;\n",
" /* Definition of color scheme for unfitted estimators */\n",
" --sklearn-color-unfitted-level-0: #fff5e6;\n",
" --sklearn-color-unfitted-level-1: #f6e4d2;\n",
" --sklearn-color-unfitted-level-2: #ffe0b3;\n",
" --sklearn-color-unfitted-level-3: chocolate;\n",
" /* Definition of color scheme for fitted estimators */\n",
" --sklearn-color-fitted-level-0: #f0f8ff;\n",
" --sklearn-color-fitted-level-1: #d4ebff;\n",
" --sklearn-color-fitted-level-2: #b3dbfd;\n",
" --sklearn-color-fitted-level-3: cornflowerblue;\n",
"\n",
" /* Specific color for light theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
" --sklearn-color-icon: #696969;\n",
"\n",
" @media (prefers-color-scheme: dark) {\n",
" /* Redefinition of color scheme for dark theme */\n",
" --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
" --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
" --sklearn-color-icon: #878787;\n",
" }\n",
"}\n",
"\n",
"#sk-container-id-1 {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"#sk-container-id-1 pre {\n",
" padding: 0;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-hidden--visually {\n",
" border: 0;\n",
" clip: rect(1px 1px 1px 1px);\n",
" clip: rect(1px, 1px, 1px, 1px);\n",
" height: 1px;\n",
" margin: -1px;\n",
" overflow: hidden;\n",
" padding: 0;\n",
" position: absolute;\n",
" width: 1px;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-dashed-wrapped {\n",
" border: 1px dashed var(--sklearn-color-line);\n",
" margin: 0 0.4em 0.5em 0.4em;\n",
" box-sizing: border-box;\n",
" padding-bottom: 0.4em;\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-container {\n",
" /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
" but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
" so we also need the `!important` here to be able to override the\n",
" default hidden behavior on the sphinx rendered scikit-learn.org.\n",
" See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
" display: inline-block !important;\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-text-repr-fallback {\n",
" display: none;\n",
"}\n",
"\n",
"div.sk-parallel-item,\n",
"div.sk-serial,\n",
"div.sk-item {\n",
" /* draw centered vertical line to link estimators */\n",
" background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
" background-size: 2px 100%;\n",
" background-repeat: no-repeat;\n",
" background-position: center center;\n",
"}\n",
"\n",
"/* Parallel-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item::after {\n",
" content: \"\";\n",
" width: 100%;\n",
" border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
" flex-grow: 1;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel {\n",
" display: flex;\n",
" align-items: stretch;\n",
" justify-content: center;\n",
" background-color: var(--sklearn-color-background);\n",
" position: relative;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item {\n",
" display: flex;\n",
" flex-direction: column;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
" align-self: flex-end;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
" align-self: flex-start;\n",
" width: 50%;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
" width: 0;\n",
"}\n",
"\n",
"/* Serial-specific style estimator block */\n",
"\n",
"#sk-container-id-1 div.sk-serial {\n",
" display: flex;\n",
" flex-direction: column;\n",
" align-items: center;\n",
" background-color: var(--sklearn-color-background);\n",
" padding-right: 1em;\n",
" padding-left: 1em;\n",
"}\n",
"\n",
"\n",
"/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
"clickable and can be expanded/collapsed.\n",
"- Pipeline and ColumnTransformer use this feature and define the default style\n",
"- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
"*/\n",
"\n",
"/* Pipeline and ColumnTransformer style (default) */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable {\n",
" /* Default theme specific background. It is overwritten whether we have a\n",
" specific estimator or a Pipeline/ColumnTransformer */\n",
" background-color: var(--sklearn-color-background);\n",
"}\n",
"\n",
"/* Toggleable label */\n",
"#sk-container-id-1 label.sk-toggleable__label {\n",
" cursor: pointer;\n",
" display: block;\n",
" width: 100%;\n",
" margin-bottom: 0;\n",
" padding: 0.5em;\n",
" box-sizing: border-box;\n",
" text-align: center;\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
" /* Arrow on the left of the label */\n",
" content: \"▸\";\n",
" float: left;\n",
" margin-right: 0.25em;\n",
" color: var(--sklearn-color-icon);\n",
"}\n",
"\n",
"#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
" color: var(--sklearn-color-text);\n",
"}\n",
"\n",
"/* Toggleable content - dropdown */\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content {\n",
" max-height: 0;\n",
" max-width: 0;\n",
" overflow: hidden;\n",
" text-align: left;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content pre {\n",
" margin: 0.2em;\n",
" border-radius: 0.25em;\n",
" color: var(--sklearn-color-text);\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
" /* Expand drop-down */\n",
" max-height: 200px;\n",
" max-width: 100%;\n",
" overflow: auto;\n",
"}\n",
"\n",
"#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
" content: \"▾\";\n",
"}\n",
"\n",
"/* Pipeline/ColumnTransformer-specific style */\n",
"\n",
"#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator-specific style */\n",
"\n",
"/* Colorize estimator box */\n",
"#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
"#sk-container-id-1 div.sk-label label {\n",
" /* The background is the default theme color */\n",
" color: var(--sklearn-color-text-on-default-background);\n",
"}\n",
"\n",
"/* On hover, darken the color of the background */\n",
"#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"/* Label box, darken color on hover, fitted */\n",
"#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
" color: var(--sklearn-color-text);\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Estimator label */\n",
"\n",
"#sk-container-id-1 div.sk-label label {\n",
" font-family: monospace;\n",
" font-weight: bold;\n",
" display: inline-block;\n",
" line-height: 1.2em;\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-label-container {\n",
" text-align: center;\n",
"}\n",
"\n",
"/* Estimator-specific */\n",
"#sk-container-id-1 div.sk-estimator {\n",
" font-family: monospace;\n",
" border: 1px dotted var(--sklearn-color-border-box);\n",
" border-radius: 0.25em;\n",
" box-sizing: border-box;\n",
" margin-bottom: 0.5em;\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-0);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-0);\n",
"}\n",
"\n",
"/* on hover */\n",
"#sk-container-id-1 div.sk-estimator:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-2);\n",
"}\n",
"\n",
"#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-2);\n",
"}\n",
"\n",
"/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
"\n",
"/* Common style for \"i\" and \"?\" */\n",
"\n",
".sk-estimator-doc-link,\n",
"a:link.sk-estimator-doc-link,\n",
"a:visited.sk-estimator-doc-link {\n",
" float: right;\n",
" font-size: smaller;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1em;\n",
" height: 1em;\n",
" width: 1em;\n",
" text-decoration: none !important;\n",
" margin-left: 1ex;\n",
" /* unfitted */\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted,\n",
"a:link.sk-estimator-doc-link.fitted,\n",
"a:visited.sk-estimator-doc-link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
".sk-estimator-doc-link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover,\n",
"div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
".sk-estimator-doc-link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"/* Span, style for the box shown on hovering the info icon */\n",
".sk-estimator-doc-link span {\n",
" display: none;\n",
" z-index: 9999;\n",
" position: relative;\n",
" font-weight: normal;\n",
" right: .2ex;\n",
" padding: .5ex;\n",
" margin: .5ex;\n",
" width: min-content;\n",
" min-width: 20ex;\n",
" max-width: 50ex;\n",
" color: var(--sklearn-color-text);\n",
" box-shadow: 2pt 2pt 4pt #999;\n",
" /* unfitted */\n",
" background: var(--sklearn-color-unfitted-level-0);\n",
" border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link.fitted span {\n",
" /* fitted */\n",
" background: var(--sklearn-color-fitted-level-0);\n",
" border: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"\n",
".sk-estimator-doc-link:hover span {\n",
" display: block;\n",
"}\n",
"\n",
"/* \"?\"-specific style due to the `<a>` HTML tag */\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link {\n",
" float: right;\n",
" font-size: 1rem;\n",
" line-height: 1em;\n",
" font-family: monospace;\n",
" background-color: var(--sklearn-color-background);\n",
" border-radius: 1rem;\n",
" height: 1rem;\n",
" width: 1rem;\n",
" text-decoration: none;\n",
" /* unfitted */\n",
" color: var(--sklearn-color-unfitted-level-1);\n",
" border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted {\n",
" /* fitted */\n",
" border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
" color: var(--sklearn-color-fitted-level-1);\n",
"}\n",
"\n",
"/* On hover */\n",
"#sk-container-id-1 a.estimator_doc_link:hover {\n",
" /* unfitted */\n",
" background-color: var(--sklearn-color-unfitted-level-3);\n",
" color: var(--sklearn-color-background);\n",
" text-decoration: none;\n",
"}\n",
"\n",
"#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
" /* fitted */\n",
" background-color: var(--sklearn-color-fitted-level-3);\n",
"}\n",
"</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
],
"text/plain": [
"SVC(kernel='linear')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svm_model = SVC(kernel='linear', C=1.0)\n",
"svm_model.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9\n"
]
}
],
"source": [
"# Vorhersagen auf dem Testset\n",
"y_pred = svm_model.predict(X_test)\n",
"\n",
"# Evaluierung der Genauigkeit\n",
"print(\"Accuracy:\", accuracy_score(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_decision_boundary(X_train, y_train, svm_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Aufgabe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Verwende nun die folgenden Datasets und versuche die bestmögliche Performance zu erreichen.\n",
"* Lade dazu das Dataset mit den bekannten Methoden (Laden mit Hilfe von `pd.read_csv`)\n",
"* Überlege, wie du bei schlechter Performance diese verbessern kannst. Zum Beipsiel: Normalisieren, Ausreißer entfernen etc.\n",
"* Müssen wir ggf. Features entfernen?\n",
"* Gehören ggf. Features mit einem Ordinal-Encoder oder mit einem Onehot-Encoder encodiert?\n",
"* Verwende für jedes Dataset eigene Code-Zellen und dokumentiere für die verschiedenen Durchläufe die Ergebnisse (zBsp. Accuracy, Confusion Matrix oder den MSE)\n",
"\n",
"**Datasets:**\n",
"* Breast Cancer `breast_cancer.csv` (verwendet von https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic)\n",
"* Diabetes `diabetes.csv` (verwendet von https://www.kaggle.com/uciml/pima-indians-diabetes-database)\n",
"* Stroke Prediction `stroke.csv` (verwendet von https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset)\n",
"\n",
"*Hinweis:* Überlege dir stets, welchen Problemtyp du verwendest und verwende dementsprechend das richtige Model! "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9824561403508771\n",
"Accuracy: 0.956140350877193\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import datasets\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.svm import SVC\n",
"from sklearn.metrics import accuracy_score, classification_report\n",
"ds = pd.read_csv(\"../../_data/breast_cancer.csv\") # ggf. etwas anders als unser bisheriges Dataset\n",
"ds = ds.dropna()\n",
"y = ds.Diagnosis\n",
"X = ds.drop('Diagnosis', axis=1)\n",
"X = X.select_dtypes(include=[np.number])\n",
"\n",
"# Aufteilen der Daten in Trainings- und Testset (80% Training, 20% Test)\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"# SVM-Modell mit RBF-Kernel\n",
"svm_model = SVC(kernel='rbf', gamma='scale', C=100)\n",
"svm_model = svm_model.fit(X_train, y_train)\n",
"# Vorhersagen auf dem Testset\n",
"y_pred = svm_model.predict(X_test)\n",
"\n",
"# Evaluierung der Genauigkeit\n",
"print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
"# plot_decision_boundary(X_train, y_train, svm_model)\n",
"\n",
"svm_model = SVC(kernel='linear', C=1.0)\n",
"svm_model.fit(X_train, y_train)\n",
"# Vorhersagen auf dem Testset\n",
"y_pred = svm_model.predict(X_test)\n",
"\n",
"# Evaluierung der Genauigkeit\n",
"print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
"# plot_decision_boundary(X_train, y_train, svm_model)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.7662337662337663\n",
"Accuracy: 0.7532467532467533\n"
]
}
],
"source": [
"ds = pd.read_csv(\"../../_data/diabetes.csv\") # ggf. etwas anders als unser bisheriges Dataset\n",
"ds = ds.dropna()\n",
"y = ds.Outcome\n",
"X = ds.drop('Outcome', axis=1)\n",
"X = X.select_dtypes(include=[np.number])\n",
"\n",
"# Aufteilen der Daten in Trainings- und Testset (80% Training, 20% Test)\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"# SVM-Modell mit RBF-Kernel\n",
"svm_model = SVC(kernel='rbf', gamma='scale', C=1.0)\n",
"svm_model = svm_model.fit(X_train, y_train)\n",
"# Vorhersagen auf dem Testset\n",
"y_pred = svm_model.predict(X_test)\n",
"\n",
"# Evaluierung der Genauigkeit\n",
"print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
"# plot_decision_boundary(X_train, y_train, svm_model)\n",
"\n",
"svm_model = SVC(kernel='linear', C=1.0)\n",
"svm_model.fit(X_train, y_train)\n",
"# Vorhersagen auf dem Testset\n",
"y_pred = svm_model.predict(X_test)\n",
"\n",
"# Evaluierung der Genauigkeit\n",
"print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
"# plot_decision_boundary(X_train, y_train, svm_model)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9460285132382892\n",
"Accuracy: 0.945010183299389\n"
]
}
],
"source": [
"ds = pd.read_csv(\"../../_data/stroke.csv\") # ggf. etwas anders als unser bisheriges Dataset\n",
"ds = ds.dropna()\n",
"y = ds.stroke\n",
"X = ds.drop('stroke', axis=1)\n",
"X = X.select_dtypes(include=[np.number])\n",
"\n",
"# Aufteilen der Daten in Trainings- und Testset (80% Training, 20% Test)\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"# SVM-Modell mit RBF-Kernel\n",
"svm_model = SVC(kernel='rbf', gamma='scale', C=1.0)\n",
"svm_model = svm_model.fit(X_train, y_train)\n",
"# Vorhersagen auf dem Testset\n",
"y_pred = svm_model.predict(X_test)\n",
"\n",
"# Evaluierung der Genauigkeit\n",
"print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
"# plot_decision_boundary(X_train, y_train, svm_model)\n",
"\n",
"svm_model = SVC(kernel='linear', C=1.0)\n",
"svm_model.fit(X_train, y_train)\n",
"# Vorhersagen auf dem Testset\n",
"y_pred = svm_model.predict(X_test)\n",
"\n",
"# Evaluierung der Genauigkeit\n",
"print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
"# plot_decision_boundary(X_train, y_train, svm_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_decision_boundary(X, y, model):\n",
" h = .02 # Schrittweite für das Meshgrid\n",
" x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
" y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
"\n",
" Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
" Z = Z.reshape(xx.shape)\n",
"\n",
" plt.contourf(xx, yy, Z, alpha=0.3)\n",
" plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o')\n",
" plt.xlabel('Feature 1')\n",
" plt.ylabel('Feature 2')\n",
" plt.title('SVM Decision Boundary')\n",
" plt.show()"
]
}
],
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