机器学习鸢尾花各种模型准确率对比

流程

获取数据集

链接：https://pan.baidu.com/s/1RzZyXsaiJB3e611itF466Q?pwd=j484 提取码：j484 --来自百度网盘超级会员V1的分享 1234

导入需要的包

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore') import matplotlib as mpl ## 设置属性防止中文乱码 mpl.rcParams['font.sans-serif'] = [u'SimHei'] mpl.rcParams['axes.unicode_minus'] = False # 导入各种模型 svm,knn,RidgeClassifier(),LogisticRegression(逻辑回归) # 支持向量机分类svc,最近邻居 knn,lr逻辑回归,rc # SVM=Support Vector Machine 是支持向量 # SVC=Support Vector Classification就是支持向量机用于分类，这里是分类问题所以引入SVC # SVR=Support Vector Regression.就是支持向量机用于回归分析 from sklearn.linear_model import LogisticRegression,RidgeClassifier from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier # 到这里四种方式引入完毕 # 引入sklearn的划分训练集和测试集合 from sklearn.model_selection import train_test_split # 计算模型准确率 from sklearn.metrics import accuracy_score`在这里插入代码片`

读取数据

iris_data=pd.read_csv('iris.csv', usecols=[ 1, 2, 3, 4,5]) 12

划分训练集和测试集

x = iris_data[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']] r = iris_data['species'] x_train, x_test, r_train, r_test = train_test_split(x, r, random_state=0) 123

调用各种模型

# svc训练 svm = SVC(C=1, kernel='linear') ## 模型训练 svm.fit(x_train, r_train) 12345 KNN

# knn训练 knn = KNeighborsClassifier(n_neighbors=1) # 模型训练 knn.fit(x_train, r_train) 1234

逻辑回归和RidgeClassifier

# 逻辑回归和RidgeClassifier训练 lr = LogisticRegression() rc = RidgeClassifier() # 模型训练 lr.fit(x_train, r_train) rc.fit(x_train, r_train) 123456

得到四个模型的测试集合准确度

# 得到4个模型测试集准确率 svm_score2 = accuracy_score(r_test, svm.predict(x_test)) lr_score2 = accuracy_score(r_test, lr.predict(x_test)) rc_score2 = accuracy_score(r_test, rc.predict(x_test)) knn_score2 = accuracy_score(r_test, knn.predict(x_test)) 12345678

print(svm_score2) print(lr_score2) print(rc_score2) print(knn_score2) #0.9736842105263158 #0.9736842105263158 #0.7631578947368421 #0.9736842105263158 12345678 绘图比较

# 绘图得到四个对比数据 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() plt.show() 123456789101112131415

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