# 将分类变量转换为因子(如果需要) data['column_name'] = data['column_name'].asfactor() # 缺失值处理 data = data.fillna(method='mean') # 特征缩放 data['column_name'] = data['column_name'].scale() # 特征选择(可选) # 使用相关性矩阵选择特征 correlation_matrix = data.cor().abs() ...
train[,y]<-as.factor(train[,y])test[,y]<-as.factor(test[,y])# Train a Deep Learning model and valid system.time(model_cv<-h2o.deeplearning(x=x,y=y,training_frame=train,distribution="multinomial",activation="Rectifier",hidden=c(32),l1=1e-5,epochs=200)) 三、最简单的案例——基于...
hf['Survived']=hf['Survived'].asfactor() predictors=hf.drop('Survived').columns response='Survived' #切分数据集,添加停止条件参数为最大模型数和最大时间,然后训练 train_hf,valid_hf=hf.split_frame(ratios=[.8],seed=1234) aml=H2OAutoML( ...
对于二分类问题,响应变量应该是一个factor类型(在JAVA中为enum类型,Python中的Pandas为categorial类型),用户可以在使用h2o.importFile函数时指定列的类型,你也可以按照如下方法指定列类型: train[,y] <- as.factor(train[,y]) test[,y] <- as.factor(test[,y]) ...
train[y] = train[y].asfactor() test[y] = test[y].asfactor() # Run AutoML for 30 seconds aml = H2OAutoML(max_runtime_secs = 30) aml.train(x = x, y = y, training_frame = train, leaderboard_frame = test) # View the AutoML Leaderboard ...
data[sentiment]=data[sentiment].asfactor() #分割数据集 train,test=data.split_frame(ratios=[0.8]) #设置模型参数 aml=H2OAutoML(max_models=10,seed=1) aml.train(y=sentiment,training_frame=train) #预测 predictions=aml.predict(test) 5.1.4模型训练与评估 使用H2O的AutoML功能,我们可以自动训练和选择...
test[,y] <- as.factor(test[,y]) # Train a Deep Learning model and valid system.time( model_cv <- h2o.deeplearning(x = x, y = y, training_frame = train, distribution = "multinomial", activation = "Rectifier", hidden = c(32), ...
问使用H2O实现网格搜索时出现服务器错误Water.exceptions.H2OIllegalArgumentExceptionENStruts has detected an unhandled exception: Messages: No result defined for action geekfly.action.LoginAction and result input Stacktraces No result defined for action geekfly.action.LoginAction and result input ...
asfactor() predictors = hf.drop('Survived').columns response = 'Survived' # 切分数据集,添加停止条件参数为最大模型数和最大时间,然后训练 train_hf, valid_hf = hf.split_frame(ratios=[.8], seed=1234) aml = H2OAutoML( max_models=20, max_runtime_secs=300, seed=1234, ) aml.train(x=...
H2O的深度学习基于多层前馈人工神经网络,该网络使用反向传播进行随机梯度下降训练。前馈人工神经网络 (ANN) 模型,也称为深度神经网络 (DNN) 或多层感知器 (MLP),是最常见的深度神经网络类型。 1、加载包和数据 library(h2o) h2o.init() df <- h2o.importFile("MPE.csv") df$MPE <- h2o.asfactor(df$MPE) ...