机器学习各种算法结果可视化比对

代码执行前首先要安装所包含的各种模块

print(__doc__)# Modified for documentation by Jaques Grobler# License: BSD 3 clauseimport numpy as npimport matplotlib.pyplot as pltfrom matplotlib.colors import ListedColormapfrom sklearn.cross_validation import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.datasets import make_moons, make_circles, make_classificationfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.svm import SVCfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifierfrom sklearn.naive_bayes import GaussianNBfrom sklearn.lda import LDAfrom sklearn.qda import QDAh = .02  # step size in the meshnames = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",         "Random Forest", "AdaBoost", "Naive Bayes", "LDA", "QDA"]classifiers = [    KNeighborsClassifier(3),    SVC(kernel="linear", C=0.025),    SVC(gamma=2, C=1),    DecisionTreeClassifier(max_depth=5),    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),    AdaBoostClassifier(),    GaussianNB(),    LDA(),    QDA()]X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,                           random_state=1, n_clusters_per_class=1)rng = np.random.RandomState(2)X += 2 * rng.uniform(size=X.shape)linearly_separable = (X, y)datasets = [make_moons(noise=0.3, random_state=0),            make_circles(noise=0.2, factor=0.5, random_state=1),            linearly_separable            ]figure = plt.figure(figsize=(27, 9))i = 1# iterate over datasetsfor ds in datasets:    # preprocess dataset, split into training and test part    X, y = ds    X = StandardScaler().fit_transform(X)    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),                         np.arange(y_min, y_max, h))    # just plot the dataset first    cm = plt.cm.RdBu    cm_bright = ListedColormap(['#FF0000', '#0000FF'])    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)    # Plot the training points    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)    # and testing points    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)    ax.set_xlim(xx.min(), xx.max())    ax.set_ylim(yy.min(), yy.max())    ax.set_xticks(())    ax.set_yticks(())    i += 1    # iterate over classifiers    for name, clf in zip(names, classifiers):        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)        clf.fit(X_train, y_train)        score = clf.score(X_test, y_test)        # Plot the decision boundary. For that, we will assign a color to each        # point in the mesh [x_min, m_max]x[y_min, y_max].        if hasattr(clf, "decision_function"):            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])        else:            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]        # Put the result into a color plot        Z = Z.reshape(xx.shape)        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)        # Plot also the training points        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)        # and testing points        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,                   alpha=0.6)        ax.set_xlim(xx.min(), xx.max())        ax.set_ylim(yy.min(), yy.max())        ax.set_xticks(())        ax.set_yticks(())        ax.set_title(name)        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),                size=15, horizontalalignment='right')        i += 1figure.subplots_adjust(left=.02, right=.98)plt.show()

效果如下：