In our regression tutorials, you can tackle realistic prediction problems by using several Hivemall's regression features such as:
train_regression function enables you to solve the regression problems with flexible configureable options. Let us try the function below.
This feature is supported from Hivemall v0.5-rc.1 or later.
create table e2006tfidf_regression_model as select feature, avg(weight) as weight from ( select train_regression(features,target,'-loss squaredloss -opt AdaGrad -reg no') as (feature,weight) from e2006tfidf_train_x3 ) t group by feature;
Prediction & evaluation
WITH predict as ( select t.rowid, sum(m.weight * t.value) as predicted from e2006tfidf_test_exploded t LEFT OUTER JOIN e2006tfidf_regression_model m ON (t.feature = m.feature) group by t.rowid ), submit as ( select t.target as actual, p.predicted as predicted from e2006tfidf_test t JOIN predict p on (t.rowid = p.rowid) ) select rmse(predicted, actual) from submit;