섹션 18. 서포트 벡터 머신 (Support Vector Machine, SVM)

여러가지/Python

섹션 18. 서포트 벡터 머신 (Support Vector Machine, SVM)

15June 2024. 8. 5. 22:14

● URL : https://colab.research.google.com/drive/1G8T1bcz_-9dWCQegIgs7TWcny-8kRoLL

Step 1. Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

Step 2. Importing the dataset

dataset = pd.read_csv('Social_Network_Ads.csv')

X = dataset.iloc[:, 1:-1].values

y = dataset.iloc[:, -1].values

Step 3. Splitting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

Step 4. Feature Scaling

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

Step 5. Training the SVM model on the Training set

- SVC : 커널 SVM 모델

from sklearn.svm import SVC

classifier = SVC(kernel = 'linear', random_state = 0)

classifier.fit(X_train, y_train)

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전형적인 SVM 모델인 선형 커널 혹은 비선형 커널(RBF, 다항)을 포함한다.

(1) kernel = 'linear' : 선형 커널 선택

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Step 6. Predicting a new result - 단일 결과 예측

print(classifier.predict(sc.transform([[30,87000]])))

Step 7. Predicting the Test set results

y_pred = classifier.predict(X_test)

print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))

Step 8. Making the Confusion Matrix

from sklearn.metrics import confusion_matrix, accuracy_score

cm = confusion_matrix(y_test, y_pred)

print(cm)

accuracy_score(y_test, y_pred)

Step 9. Visualising the Training set results

from matplotlib.colors import ListedColormap

X_set, y_set = sc.inverse_transform(X_train), y_train

X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 10, stop = X_set[:, 0].max() + 10, step = 1),

np.arange(start = X_set[:, 1].min() - 1000, stop = X_set[:, 1].max() + 1000, step = 1))

plt.contourf(X1, X2, classifier.predict(sc.transform(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),

alpha = 0.75, cmap = ListedColormap(('salmon', 'dodgerblue')))

plt.xlim(X1.min(), X1.max())

plt.ylim(X2.min(), X2.max())

for i, j in enumerate(np.unique(y_set)):

plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c = ListedColormap(('salmon', 'dodgerblue'))(i), label = j)

plt.title('K-NN (Training set)')

plt.xlabel('Age')

plt.ylabel('Estimated Salary')

plt.legend()

plt.show()

Step 10. Visualising the Test set results