我试图找出一种方法来优化我的查询,因为我需要超过48小时来执行脚本,因为我有一个庞大的数据库。我尝试在所需的表中创建所有可能的索引。我甚至试图将查询分解为子查询,但仍然没有改善执行时间。任何意见或想法都表示赞赏。这是我的疑问:
data_unb = data[1:120,:] # messing up with target variable
train, valid = data_unb.split_frame([0.8], seed=12345)
m1 = h2o.estimators.random_forest.H2ORandomForestEstimator(seed=12345)
m2 = h2o.estimators.random_forest.H2ORandomForestEstimator(balance_classes=True, seed=12345)
m3 = h2o.estimators.random_forest.H2ORandomForestEstimator(balance_classes=True, class_sampling_factors=[1.0,1.0,2.5], seed=12345)
m1.train(x=list(range(4)),y=4,training_frame=train,validation_frame=valid,model_id='RF_defaults')
m2.train(x=list(range(4)),y=4,training_frame=train,validation_frame=valid,model_id='RF_balanced')
m3.train(x=list(range(4)),y=4,training_frame=train,validation_frame=valid,model_id='RF_class_sampling',)
m1.confusion_matrix(train)
m2.confusion_matrix(train)
m3.confusion_matrix(train)