人工智能|机器学习算法之鸢尾花数据不同分类器效果比较机器学习|svm|支持向量机|机器

【人工智能|机器学习算法之鸢尾花数据不同分类器效果比较】工程代码完整已上传：鸢尾花数据不同分类器效果比较

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下面言归正传：使用线性SVM、 LogisticRegression、RidgeClassifier、knn对鸢尾花数据进行分类效果对比

import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt from sklearn.svm import SVC from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.linear_model import LogisticRegression,RidgeClassifier from sklearn.neighbors import KNeighborsClassifier

## 设置属性防止中文乱码 mpl.rcParams['font.sans-serif'] = [u'SimHei'] mpl.rcParams['axes.unicode_minus'] = False

## 读取数据 # 'sepal length', 'sepal width', 'petal length', 'petal width' iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度' path = './datas/iris.data'# 数据文件路径 data = https://www.it610.com/article/pd.read_csv(path, header=None) x, y = data[list(range(4))], data[4] y = pd.Categorical(y).codes x = x[[0, 1]]

## 数据分割 x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=28, train_size=0.6)

## 数据SVM分类器构建 svm = SVC(C=1, kernel='linear')## 模型训练 svm.fit(x_train, y_train)

SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape=None, degree=3, gamma=‘auto’, kernel=‘linear’,
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)

svm.intercept_

array([ 4.79987796, 6.63529625, 12.07494136])

## Linear分类器构建 lr = LogisticRegression() rc = RidgeClassifier()#ridge是为了解决特征大于样本，而导致分类效果较差的情况，而提出的 #svm有一个重要的瓶颈——当特征数大于样本数的时候，效果变差 knn = KNeighborsClassifier()## 模型训练 lr.fit(x_train, y_train) rc.fit(x_train, y_train) knn.fit(x_train, y_train)

KNeighborsClassifier(algorithm=‘auto’, leaf_size=30, metric=‘minkowski’,
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights=‘uniform’)

### 效果评估 svm_score1 = accuracy_score(y_train, svm.predict(x_train)) svm_score2 = accuracy_score(y_test, svm.predict(x_test))lr_score1 = accuracy_score(y_train, lr.predict(x_train)) lr_score2 = accuracy_score(y_test, lr.predict(x_test))rc_score1 = accuracy_score(y_train, rc.predict(x_train)) rc_score2 = accuracy_score(y_test, rc.predict(x_test))knn_score1 = accuracy_score(y_train, knn.predict(x_train)) knn_score2 = accuracy_score(y_test, knn.predict(x_test))## 画图 x_tmp = [0,1,2,3] y_score1 = [svm_score1, lr_score1, rc_score1, knn_score1] y_score2 = [svm_score2, lr_score2, rc_score2, knn_score2]plt.figure(facecolor='w') plt.plot(x_tmp, y_score1, 'r-', lw=2, label=u'训练集准确率') plt.plot(x_tmp, y_score2, 'g-', lw=2, label=u'测试集准确率') plt.xlim(0, 3) plt.ylim(np.min((np.min(y_score1), np.min(y_score2)))*0.9, np.max((np.max(y_score1), np.max(y_score2)))*1.1) plt.legend(loc = 'lower right') plt.title(u'鸢尾花数据不同分类器准确率比较', fontsize=16) plt.xticks(x_tmp, [u'SVM', u'Logistic', u'Ridge', u'KNN'], rotation=0) plt.grid(b=True) plt.show()

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### 画图比较 N = 500 x1_min, x2_min = x.min() x1_max, x2_max = x.max()t1 = np.linspace(x1_min, x1_max, N) t2 = np.linspace(x2_min, x2_max, N) x1, x2 = np.meshgrid(t1, t2)# 生成网格采样点 grid_show = np.dstack((x1.flat, x2.flat))[0] # 测试点## 获取各个不同算法的测试值 svm_grid_hat = svm.predict(grid_show) svm_grid_hat = svm_grid_hat.reshape(x1.shape)# 使之与输入的形状相同lr_grid_hat = lr.predict(grid_show) lr_grid_hat = lr_grid_hat.reshape(x1.shape)# 使之与输入的形状相同rc_grid_hat = rc.predict(grid_show) rc_grid_hat = rc_grid_hat.reshape(x1.shape)# 使之与输入的形状相同knn_grid_hat = knn.predict(grid_show) knn_grid_hat = knn_grid_hat.reshape(x1.shape)# 使之与输入的形状相同## 画图 cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF']) cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b']) plt.figure(facecolor='w', figsize=(14,7))### svm plt.subplot(221) ## 区域图 plt.pcolormesh(x1, x2, svm_grid_hat, cmap=cm_light) ## 所以样本点 plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)# 样本 ## 测试数据集 plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)# 圈中测试集样本 ## lable列表 plt.xlabel(iris_feature[0], fontsize=13) plt.ylabel(iris_feature[1], fontsize=13) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.title(u'鸢尾花SVM特征分类', fontsize=16) plt.grid(b=True, ls=':') plt.tight_layout(pad=1.5)plt.subplot(222) ## 区域图 plt.pcolormesh(x1, x2, lr_grid_hat, cmap=cm_light) ## 所以样本点 plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)# 样本 ## 测试数据集 plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)# 圈中测试集样本 ## lable列表 plt.xlabel(iris_feature[0], fontsize=13) plt.ylabel(iris_feature[1], fontsize=13) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.title(u'鸢尾花Logistic特征分类', fontsize=16) plt.grid(b=True, ls=':') plt.tight_layout(pad=1.5)plt.subplot(223) ## 区域图 plt.pcolormesh(x1, x2, rc_grid_hat, cmap=cm_light) ## 所以样本点 plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)# 样本 ## 测试数据集 plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)# 圈中测试集样本 ## lable列表 plt.xlabel(iris_feature[0], fontsize=13) plt.ylabel(iris_feature[1], fontsize=13) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.title(u'鸢尾花Ridge特征分类', fontsize=16) plt.grid(b=True, ls=':') plt.tight_layout(pad=1.5)plt.subplot(224) ## 区域图 plt.pcolormesh(x1, x2, knn_grid_hat, cmap=cm_light) ## 所以样本点 plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)# 样本 ## 测试数据集 plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)# 圈中测试集样本 ## lable列表 plt.xlabel(iris_feature[0], fontsize=13) plt.ylabel(iris_feature[1], fontsize=13) plt.xlim(x1_min, x1_max) plt.ylim(x2_min, x2_max) plt.title(u'鸢尾花KNN特征分类', fontsize=16) plt.grid(b=True, ls=':') plt.tight_layout(pad=1.5)plt.show()