Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with scikit-learn
Search options not deleted
user 80860
scikit-learn is a popular machine learning package for Python that has simple and efficient tools for predictive data analysis. Topics include classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
1
vote
How can I use multiple features in basic sentiment analysis in scikit-learn?
You can one-hot encode the gender column and append it to your tfidf table.
gender = pd.get_dummies(training_data['gender-column'])
X = text_tfidf.join(gender)
- 77
2
votes
Accepted
Normal distribution and Random Forest
If you use tree-based algorithms like random forests the data distribution should not be an issue. Linear algorithms are more dependent on the distribution of your variables. To check if you overfit c …
- 77