Q:银行分控模型的建立。包括训练的结果,训练的误差,画出混淆矩阵,不少于两种方法
1.逻辑回归
1 import pandas as pd 2 import numpy as np 3 # 参数初始化 4 inputfile = '../data/bankloan.xls' 5 data = pd.read_excel(inputfile) 6 X = data.drop(columns='违约') 7 y = data['违约'] 8 from sklearn.model_selection import train_test_split 9 from sklearn.linear_model import LogisticRegression 10 11 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1) 12 13 model = LogisticRegression() 14 model.fit(X_train, y_train) 15 y_pred = model.predict(X_test) 16 from sklearn.metrics import accuracy_score 17 score = accuracy_score(y_pred, y_test) 18 print(score) 19 20 21 #混淆矩阵 22 def cm_plot(y, y_pred): 23 from sklearn.metrics import confusion_matrix #导入混淆矩阵函数 24 cm = confusion_matrix(y, y_pred) #混淆矩阵 25 import matplotlib.pyplot as plt #导入作图库 26 plt.matshow(cm, cmap=plt.cm.Greens) #画混淆矩阵图,配色风格使用cm.Greens,更多风格请参考官网。 27 plt.colorbar() #颜色标签 28 for x in range(len(cm)): #数据标签 29 for y in range(len(cm)): 30 plt.annotate(cm[x,y], xy=(x, y), horizontalalignment='center', verticalalignment='center') 31 plt.ylabel('True label') #坐标轴标签 32 plt.xlabel('Predicted label') #坐标轴标签 33 return plt 34 cm_plot(y_test, y_pred)
2神经网络
# -*- coding: utf-8 -*- """ Created on Sun Mar 27 19:10:46 2022 @author: 123 """ import pandas as pd import numpy as np from sklearn.model_selection import train_test_split inputfile = './bankloan.xls' data = pd.read_excel(inputfile) X = data.drop(columns='违约') y = data['违约'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1) from keras.models import Sequential from keras.layers.core import Dense, Activation from keras.layers import Activation, Dense, Dropout model = Sequential() model.add(Dense(64,input_dim=8,activation='relu')) #8个特征维度 # Drop防止过拟合的数据处理方式 model.add(Dropout(0.5)) model.add(Dense(64,activation='relu')) model.add(Dropout(0.5)) model.add(Dense(1,activation='sigmoid')) # 编译模型,定义损失函数,优化函数,绩效评估函数 model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) #二元分类,所以指定损失函数为binary_crossentropy # 导入数据进行训练 model.fit(X_train,y_train,epochs=200,batch_size=128) #yp = model.predict_classes(X_test).reshape(len(y_test)) # 分类预测 #print(yp) predict_x=model.predict(X_test) classes_x=np.argmax(predict_x,axis=1) score = model.evaluate(X_test,y_test,batch_size=128) print(score) def cm_plot(y, y_pred): from sklearn.metrics import confusion_matrix #导入混淆矩阵函数 cm = confusion_matrix(y, y_pred) #混淆矩阵 import matplotlib.pyplot as plt #导入作图库 plt.matshow(cm, cmap=plt.cm.Greens) #画混淆矩阵图,配色风格使用cm.Greens,更多风格请参考官网。 plt.colorbar() #颜色标签 for x in range(len(cm)): #数据标签 for y in range(len(cm)): plt.annotate(cm[x,y], xy=(x, y), horizontalalignment='center', verticalalignment='center') plt.ylabel('True label') #坐标轴标签 plt.xlabel('Predicted label') #坐标轴标签 return plt cm_plot(y_test,classes_x)