I am working on a binary text classification problem. As the classes are highly imbalanced, I am using sampling techniques like RandomOversampler()
. Then for classification I would use RandomForestClassifier()
whose parameters need to be tuned using GridSearchCV()
.
I am trying to create a pipeline to do these in order but failed so far. It throws invalid parameters
.
param_grid = { 'n_estimators': [5, 10, 15, 20], 'max_depth': [2, 5, 7, 9] } grid_pipe = make_pipeline(RandomOverSampler(),RandomForestClassifier()) grid_searcher = GridSearchCV(grid_pipe,param_grid,cv=10) grid_searcher.fit(tfidf_train[predictors],tfidf_train[target])
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Answer
The parameters you defined in the params
is for RandomForestClassifier, but in the gridSearchCV, you are not passing a RandomForestClassifier
object.
You are passing a pipeline object, for which you have to rename the parameters to access the internal RandomForestClassifier object.
Change them to:
param_grid = { 'randomforestclassifier__n_estimators': [5, 10, 15, 20], 'randomforestclassifier__max_depth': [2, 5, 7, 9] }
And it will work.