如下10种分类算法对比Classifier comparison

如下10种分类算法对比

names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process","Decision Tree", "Random Forest", "Neural Net", "AdaBoost","Naive Bayes", "QDA"]

https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.htmlprint(__doc__)# Code source: Gaël Varoquaux
#              Andreas Müller
# Modified for documentation by Jaques Grobler
# License: BSD 3 clauseimport numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysish = .02  # step size in the meshnames = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process","Decision Tree", "Random Forest", "Neural Net", "AdaBoost","Naive Bayes", "QDA"]classifiers = [KNeighborsClassifier(3),SVC(kernel="linear", C=0.025),SVC(gamma=2, C=1),GaussianProcessClassifier(1.0 * RBF(1.0)),DecisionTreeClassifier(max_depth=5),RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),MLPClassifier(alpha=1, max_iter=1000),AdaBoostClassifier(),GaussianNB(),QuadraticDiscriminantAnalysis()]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 datasets
for ds_cnt, ds in enumerate(datasets):# preprocess dataset, split into training and test partX, y = dsX = StandardScaler().fit_transform(X)X_train, X_test, y_train, y_test = \train_test_split(X, y, test_size=.4, random_state=42)x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5xx, yy = np.meshgrid(np.arange(x_min, x_max, h),np.arange(y_min, y_max, h))# just plot the dataset firstcm = plt.cm.RdBucm_bright = ListedColormap(['#FF0000', '#0000FF'])ax = plt.subplot(len(datasets), len(classifiers) + 1, i)if ds_cnt == 0:ax.set_title("Input data")# Plot the training pointsax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,edgecolors='k')# Plot the testing pointsax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6,edgecolors='k')ax.set_xlim(xx.min(), xx.max())ax.set_ylim(yy.min(), yy.max())ax.set_xticks(())ax.set_yticks(())i += 1# iterate over classifiersfor 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, x_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 plotZ = Z.reshape(xx.shape)ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)# Plot the training pointsax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright,edgecolors='k')# Plot the testing pointsax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,edgecolors='k', alpha=0.6)ax.set_xlim(xx.min(), xx.max())ax.set_ylim(yy.min(), yy.max())ax.set_xticks(())ax.set_yticks(())if ds_cnt == 0:ax.set_title(name)ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),size=15, horizontalalignment='right')i += 1plt.tight_layout()
plt.show()

#result

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