y = dataset.iloc[:, 4].values
# 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)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Logistic Regression to the Training set
classifier = LogisticRegression(lr=0.001)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = np.array(classifier.predict(X_test))
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, np.array(classifier.predict(np.array([X1.ravel(), X2.ravel()]).T)).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
import numpy as np import matplotlib.pyplot as plt # import seaborn as sns from sklearn import datasets from Logistic_Regression import LogisticRegression iris = datasets.load_iris() X = iris.data[:, :2] y = (iris.target != 0) *1 clf = LogisticRegression() clf.fit(X,y) pred = clf.predict(X) plt.figure(figsize=(10, 6)) plt.scatter(X[y == 0][:, 0], X[y == 0][:, 1], color='b', label='0') plt.scatter(X[y == 1][:, 0], X[y == 1][:, 1], color='r', label='1') plt.legend() x1_min, x1_max = X[:,0].min(), X[:,0].max(), x2_min, x2_max = X[:,1].min(), X[:,1].max(), xx1, xx2 = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max)) grid = np.c_[xx1.ravel(), xx2.ravel()] probs = clf.predict_prob(grid).reshape(xx1.shape) plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors='black') plt.show()
