X_train_MGD, X_test_MGD, y_train_MGD, y_test_MGD = train_test_split(principalDf2.iloc[:,0:5],principalDf2['anomaly'],test_size=0.20, random_state=42) mu, sigma = estimate_gaussian(X_train_MGD) p_tr = multivariat
2,3,4,5],'feature2':[5,4,3,2,1],'label':[0,1,0,1,0]}df=pd.DataFrame(data)# 特征和标签X=df[['feature1','feature2']]y=df['label']# 划分数据集X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)print("训练集特征...
xtrain_base, xpred_base, ytrain_base, ypred_base = train_test_split( xtrain, ytrain, test_size=0.5, random_state=SEED) 我们现在有一个为基学习器准备的训练集(X_train_base,y_train_base)和一个预测集(X_pred_base,y_pred_base),准备好为元学习器生成预测矩阵了。 第四步:在训练集上训练...
进行Split操作 现在,我们使用.str.split()方法来将location列中的字符串分割为两个部分:城市和国家。我们可以指定分隔符为逗号,并设置expand=True以返回一个新的DataFrame。 # 使用逗号分割split_locations=df['location'].str.split(',',expand=True)# 将拆分后的列重命名split_locations.columns=['City','Count...
# identifying categorical features categorical_features = np.where(train.dtypes == 'object')[0] 然后把训练数据拆分为训练和验证集,并本地验证模型性能。 from sklearn.model_selection import train_test_split # splitting train data into training and validation set ...
df = pd.DataFrame(diabetes.data, columns=columns)# load the dataset as a pandas data frame y = diabetes.target # define the target variable (dependent variable) as y 现在我们可以用traintestsplit函数划分数据集。test_size = 0.2表示测试数据在数据集中的占比,一般情况下,训练集和测试集的比例应该...
var_filter(dat, y="creditability") # breaking dt into train and test # 将DataFrame分成训练集和测试集 train, test = sc.split_df(dt_s, 'creditability').values() # woe binning --- # 根据woe值进行分箱 bins = sc.woebin(dt_s, y="creditability") # sc.woebin_plot(bins) # binning...
Create a function called split_data to split the data frame into test and train data. The function should take the dataframe df as a parameter, and return a dictionary containing the keys train and test. Move the code under the Split Data into Training and Validation Sets heading into the ...
from azure.ai.ml import command from azure.ai.ml import Input, Output data_prep_component = command( name="data_prep_credit_defaults", display_name="Data preparation for training", description="reads a .xl input, split the input to train and test", inputs={ "data": Input(type="uri_...
train_data= split['train'] test_data= split['test'] dt= em.DTMatcher(name='DecisionTree', random_state=0) svm= em.SVMMatcher(name='SVM', random_state=0) rf= em.RFMatcher(name='RF', random_state=0) lg= em.LogRegMatcher(name='LogReg', random_state=0) ...