Scikit-Learn’s Pipeline: A sparse matrix was passed, but dense data is required

Unfortunately those two are incompatible. A CountVectorizer produces a sparse matrix and the RandomForestClassifier requires a dense matrix. It is possible to convert using X.todense(). Doing this will substantially increase your memory footprint.

Below is sample code to do this based on http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html which allows you to call .todense() in a pipeline stage.

class DenseTransformer(TransformerMixin):

    def fit(self, X, y=None, **fit_params):
        return self

    def transform(self, X, y=None, **fit_params):
        return X.todense()

Once you have your DenseTransformer, you are able to add it as a pipeline step.

pipeline = Pipeline([
     ('vectorizer', CountVectorizer()), 
     ('to_dense', DenseTransformer()), 
     ('classifier', RandomForestClassifier())
])

Another option would be to use a classifier meant for sparse data like LinearSVC.

from sklearn.svm import LinearSVC
pipeline = Pipeline([('vectorizer', CountVectorizer()), ('classifier', LinearSVC())])