机器学习29:Sklearn库常用分类器及效果比较

1.Sklearn库常用分类器：

#【1】 KNN Classifier   
# k-近邻分类器 
from sklearn.neighbors import KNeighborsClassifier
 
clf = KNeighborsClassifier()
clf.fit(train_x, train_y)


 
#【2】 Logistic Regression Classifier    
# 逻辑回归分类器
from sklearn.linear_model import LogisticRegression
 
clf = LogisticRegression(penalty='l2')
clf.fit(train_x, train_y)



#【3】 Random Forest Classifier    
# 随机森林分类器
from sklearn.ensemble import RandomForestClassifier
 
clf = RandomForestClassifier(n_estimators=8)
clf.fit(train_x, train_y)


 
#【4】 Decision Tree Classifier 
# 决策树分类器   
from sklearn import tree
 
clf = tree.DecisionTreeClassifier()
clf.fit(train_x, train_y)



#【5】 SVM Classifier  
# 支持向量机分类器  
from sklearn.svm import SVC
 
clf = SVC(kernel='rbf', probability=True)
clf.fit(train_x, train_y)



#【6】 Multinomial Naive Bayes Classifier   
# 多项式朴素贝叶斯分类器 
from sklearn.naive_bayes import MultinomialNB
 
clf = MultinomialNB(alpha=0.01)
clf.fit(train_x, train_y)



#【7】 GBDT(Gradient Boosting Decision Tree) Classifier    
# 梯度增强决策树分类器
from sklearn.ensemble import GradientBoostingClassifier
 
clf = GradientBoostingClassifier(n_estimators=200)
clf.fit(train_x, train_y)


 
#【8】AdaBoost Classifier
from sklearn.ensemble import  AdaBoostClassifier
 
clf = AdaBoostClassifier()
clf.fit(train_x, train_y)


 
#【9】 GaussianNB
# 高斯贝叶斯分类器
from sklearn.naive_bayes import GaussianNB
 
clf = GaussianNB()
clf.fit(train_x, train_y)



#【10】 Linear Discriminant Analysis
# 线性判别分析
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
 
clf = LinearDiscriminantAnalysis()
clf.fit(train_x, train_y)



#【11】 Quadratic Discriminant Analysis
# 二次判别分析
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
 
clf = QuadraticDiscriminantAnalysis()
clf.fit(train_x, train_y)

2.Slearn常见分类器的效果比较：

            本段代码摘抄自Sklearn常见分类起的效果比较，效果图可以点进原文查看，也可以在python上运行查看。

import 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 BernoulliRBM
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
# from sklearn.gaussian_process import GaussianProcess
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis

h = .02  # step size in the mesh

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

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(),
    QuadraticDiscriminantAnalysis(),
    #GaussianProcess(),
    #BernoulliRBM(),
    ]

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 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, random_state=42)

    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)
    if ds_cnt == 0:
        ax.set_title("Input data")
    # 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(())
        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 += 1

plt.tight_layout()
plt.show()