本文共 2248 字，大约阅读时间需要 7 分钟。

 

Tackling overfitting via regularization

import numpy as npfrom sklearn import datasetsfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom matplotlib.colors import ListedColormapimport matplotlib.pyplot as pltfrom sklearn.linear_model import LogisticRegressioniris = datasets.load_iris()X = iris.data[:, [2, 3]]y = iris.targetX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)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 = np.hstack((y_train, y_test))def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):    markers = ('s', 'x', 'o', '^', 'v')    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')    cmap = ListedColormap(colors[:len(np.unique(y))])    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),np.arange(x2_min, x2_max, resolution))    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)    Z = Z.reshape(xx1.shape)    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)    plt.xlim(xx1.min(), xx1.max())    plt.ylim(xx2.min(), xx2.max())    for idx, cl in enumerate(np.unique(y)):        plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1], alpha=0.8, c=colors[idx], marker=markers[idx],                     label=cl, edgecolor='black')    if test_idx:        X_test, y_test = X[test_idx, :], y[test_idx]        plt.scatter(X_test[:, 0], X_test[:, 1], c='none', edgecolor='black', alpha=1.0, linewidth=1,                    marker='o', s=100, label='test set')    weights, params = [], []for c in np.arange(-5, 5):    lr = LogisticRegression(C=10.**c, random_state=1, solver='liblinear', multi_class='ovr')    lr.fit(X_train_std, y_train)    weights.append(lr.coef_[1])    params.append(10.**c)weights = np.array(weights)plt.plot(params, weights[:, 0],         label='petal length')plt.plot(params, weights[:, 1], linestyle='--',         label='petal width')plt.ylabel('weight coefficient')plt.xlabel('C')plt.legend(loc='upper left')plt.xscale('log')plt.show()

 

 

转载地址：http://omygf.baihongyu.com/