from sklearn.ensemble import RandomForestClassifier
from plot_decision_regions_sklearn import plot_decision_regions
from sklearn import datasets
import numpy as np
from sklearn.cross_validation import train_test_split
from plot_decision_regions_sklearn import plt
from plot_decision_regions_sklearn import plot_decision_regions
iris = datasets.load_iris()
X = iris.data[:, [2,3]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
forest = RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=1, n_jobs=2)
forest.fit(X_train, y_train)
plot_decision_regions(X_combined, y_combined, classifier=forest, test_idx=range(105,150))
plt.xlabel('petal length')
plt.ylabel('petal width')
plt.legend(loc='upper left')
plt.show()
from plot_decision_regions_sklearn import plt
from plot_decision_regions_sklearn import plot_decision_regions
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=0)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0)
ppn.fit(X_train_std, y_train)
y_pred = ppn.predict(X_test_std)
print('Misclassified samples: %d' % (y_test != y_pred).sum())
print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined_std,
y=y_combined,
classifier=ppn,
test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
from sklearn.svm import SVC
from plot_decision_regions_sklearn import plot_decision_regions
import matplotlib.pyplot as plt
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=0)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined_std = np.hstack((y_train, y_test))
svm = SVC(kernel='rbf', random_state=0, gamma=0.2,
C=1.0) #gamma can be 100.0 or 0.2 to contrast results for intuition
svm.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std,
y_combined_std,
classifier=svm,
test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()