问题描述
我为二进制分类器开发了以下模型。我不得不使用roc_auc_score进行评估。我得到roc_auc_score的异常值。
使用时epoch = 1,roc_auc_score = 0.8 epoch = 2,roc_auc_score =0.53
在此之后的任何时期，它都恒定为0.5。为什么会这样呢？我的模型出问题了吗？
数据预处理
#Load Dataset
test = pd.read_csv('test.csv')
train = pd.read_csv('train.csv')
#Combine Train and Test set for Data Cleaning
train['set'] = 'train'
test['set'] = 'test'
df = pd.concat([test,train])
#One Hot Encoding
df = pd.get_dummies(df,columns=['Gender','Driving_License','PrevIoUsly_Insured','Vehicle_Age','Vehicle_damage'])
#Moving Target Column to End
target = df['Response']
df.drop(labels=['Response'],axis=1,inplace = True)
df.insert(16,'Response',target)
#Separating Train and Test Data
train = df[df['set']=='train']
test = df[df['set']=='test']
train = train.drop('set',1)
test = test.drop('set',1)
#Creating Input Features and Target Variables
X= train.iloc[:,1:15]
y= train.iloc[:,[15]]
#Standardizing the Input Features
scaler = StandardScaler()
X = scaler.fit_transform(X)
#Train Test Split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)
#Model
model = Sequential()
model.add(Dense(14,activation='relu',kernel_initializer='random_normal',input_dim=14))
#Output Layer
model.add(Dense(1,activation = 'sigmoid',kernel_initializer='random_normal'))
#Compiling the neural network
model.compile(optimizer ='adam',loss='binary_crossentropy',metrics =['accuracy'])
#Fitting the data to the training dataset
model.fit(X_train,batch_size=32,epochs=1,verbose=0)
#Make predictions and convert to binary value
pred_train = model.predict(X_train)
pred_test = model.predict(X_test)
#ROC AUC score
print('Train AUC = {:.5f}'.format(roc_auc_score(y_train,pred_train)))
print('Test AUC = {:.5f}'.format(roc_auc_score(y_test,pred_test)))
#Accuracy
print('Train Accuracy = {:.3f}'.format(accuracy_score(y_train,pred_train.round())))
print('Test Accuracy = {:.3f}'.format(accuracy_score(y_test,pred_test.round())))
解决方法
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