My objective is to classify sentences into useful (denote in boolean as 1) and not useful (denote in boolean as 0) categories.
I have about 525 features where 300 features are the most frequent and important keywords after removing stopwords and the rest are domain names.
The total number of documents I have is 793.
I manually labelled the classes as useful and not useful and I have about 93 useful and the rest (700) as not useful.
Below is the result of my logistic regression with the parameter values:
model = LogisticRegression(penalty='l1') X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0) model = model.fit(X, y)
Based on the results i got, it seems that the regression is doing a very bad job at classifying sentences as useful with really bad precision, recall and 1-score.
How can i improve the accuracy?
Note, as shown above, i already am implementing L1 regularization.