前言

本文将记录机器学习当中关于svm分类器、 K近邻分类器、决策树分类器对比，附源码及介绍。

提示：以下是本篇文章正文内容，下面案例可供参考

一、源码解析

1.引入库

代码如下（示例）：

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
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 import svm  # svm分类器
from sklearn.neighbors import KNeighborsClassifier  # K近邻分类器
from sklearn.tree import DecisionTreeClassifier  # 决策树分类器
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier  # 随机森林，Adboost，GBDT
from sklearn.linear_model import LogisticRegressionCV  # 逻辑回归

2.设置属性防止中文乱码

代码如下（示例）：

mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False

3.获得随机数生成器

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)  # 获得随机数生成器
X += 2 * rng.uniform(size=X.shape)  # 产生随机数加到X样本上
linearly_separable = (X, y)  # 生成线性的独立的数据

datasets = [make_moons(noise=0.3, random_state=0),
            make_circles(noise=0.2, factor=0.4, random_state=1),
            linearly_separable
            ]

4.建模环节，用list把所有算法装起来

names = ["Nearest Neighbors", "Logistic", "Decision Tree", "Random Forest", "AdaBoost", "GBDT", "svm"]
classifiers = [
    KNeighborsClassifier(3),
    LogisticRegressionCV(),
    DecisionTreeClassifier(max_depth=5),
    RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
    AdaBoostClassifier(n_estimators=10,learning_rate=1.5),
    GradientBoostingClassifier(n_estimators=10, learning_rate=1.5),
    svm.SVC(C=1, kernel='rbf')
    ]

5.画图

figure = plt.figure(figsize=(27, 9), facecolor='w')
i = 1
h = .02  # 步长

for ds in datasets:
    X, y = ds
    # 将数据标准化
    X = StandardScaler().fit_transform(X)
    # 测试集验证集划分
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)
    
    # 用以区域绘制
    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))
    cm = plt.cm.RdBu
    cm_bright = ListedColormap(['r', 'b', 'y'])
    
    # 原始点的绘制
    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
    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

    # 画每个算法的图
    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)
        # hasattr是判定某个模型中，有没有哪个参数，
        # 判断clf模型中，有没有decision_function
        # np.c_让内部数据按列合并
        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]

        Z = Z.reshape(xx.shape)
        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)
        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
        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=25, horizontalalignment='right')
        i += 1

## 展示图
figure.subplots_adjust(left=.02, right=.98)
plt.show()
# plt.savefig("cs.png")

6.展示图

总结

以上就是今天要讲的内容，本文仅仅简单介绍了几种分类算法比较，仅供参考学习，如果对您有所帮助，感谢点赞收藏！