逻辑回归参数详解

# -*- coding: utf-8 -*- """ Created on Tue Aug 11 10:12:48 2020 @author: Admin """ # 引入数据 from sklearn import datasets import numpy as np iris = datasets.load_iris() X = iris.data[:,[2,3]] y = iris.target print("Class labels:",np.unique(y)) #打印分类类别的种类 # 切分训练数据和测试数据 from sklearn.model_selection import train_test_split ## 30%测试数据，70%训练数据，stratify=y表示训练数据和测试数据具有相同的类别比例 X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=1,stratify=y) from sklearn.preprocessing import StandardScaler sc = StandardScaler() ## 估算训练数据中的mu和sigma sc.fit(X_train) ## 使用训练数据中的mu和sigma对数据进行标准化 X_train_std = sc.transform(X_train) X_test_std = sc.transform(X_test) ## 画出决策边界图(只有在2个特征才能画出来) import matplotlib.pyplot as plt %matplotlib inline from matplotlib.colors import ListedColormap def plot_decision_region(X,y,classifier,resolution=0.02): markers = ('s','x','o','^','v') colors = ('red','blue','lightgreen','gray','cyan') cmap = ListedColormap(colors[:len(np.unique(y))]) # plot the decision surface x1_min,x1_max = X[:,0].min()-1,X[:,0].max()+1 x2_min,x2_max = X[:,1].min()-1,X[:,1].max()+1 xx1,xx2 = np.meshgrid(np.arange(x1_min,x1_max,resolution), np.arange(x2_min,x2_max,resolution)) Z = classifier.predict(np.array([xx1.ravel(),xx2.ravel()]).T) Z = Z.reshape(xx1.shape) plt.contourf(xx1,xx2,Z,alpha=0.3,cmap=cmap) plt.xlim(xx1.min(),xx1.max()) plt.ylim(xx2.min(),xx2.max()) # plot class samples for idx,cl in enumerate(np.unique(y)): plt.scatter(x=X[y==cl,0], y = X[y==cl,1], alpha=0.8, c=colors[idx], marker = markers[idx], label=cl, edgecolors='black') #逻辑回归 由于标签有三类，特征有2个，因此截距和系数也有三对 from sklearn.linear_model import LogisticRegression lr = LogisticRegression(C=100.0,random_state=1) lr.fit(X_train_std,y_train) print("Class:",lr.classes_) print("Coef:",lr.coef_) print("intercept",lr.intercept_) print("n_iter",lr.n_iter_) ''' Class: [0 1 2] Coef: [[-5.61268224 -4.30718677] [ 2.40969576 -2.07325711] [ 9.51524418 5.39484899]] intercept [-5.8391281 -0.75730853 -9.21167569] n_iter [9] ''' plot_decision_region(X_train_std,y_train,classifier=lr,resolution=0.02) plt.xlabel('petal length [standardized]') plt.ylabel('petal width [standardized]') plt.legend(loc='upper left') plt.show() # 预测 ## 预测前三样本在各个类别的概率 print("前三样本在各个类别的预测概率为：\n",lr.predict_proba(X_test_std[:3,:])) print("\n============================") ## 获得前三个样本的分类标签 print("\n前三样本在各个类别的预测类别为：\n",lr.predict(X_test_std[:3,:])) print("\n============================") ''' 前三样本在各个类别的预测概率为： [[3.17983737e-08 1.44886616e-01 8.55113353e-01] [8.33962295e-01 1.66037705e-01 4.55557009e-12] [8.48762934e-01 1.51237066e-01 4.63166788e-13]] ============================ 前三样本在各个类别的预测类别为： [2 0 0] ============================ '''