Deleted this message from the /dev/ channel. Copying and pasting here:
I am Bhavya Bhardwaj (https://github.com/Bhavya1705). I am a student of Electronics and Communication at Amrita Vishwa Vidyapeetham, India. My thanks to you and the team for sklearn. I have been try to make some contributions to the scikit-learn library - scikit-learn/scikit-learn#5516. I have made the code, and the necessary changes to the init file and test files, in addition to the _classification file. This is the links to my commits - scikit-learn/scikit-learn#20861, as you will see, there are many mistakes, that I have made, Any help that you can render to me would be much appreciated and would be a wonderful learning experience.
Hi. I am trying to develop my own Estimator based on TransformerMixin and BaseEstimator. To make sure I am doing things right I have added a test to my project :
import MyEstimator from sklearn.utils.estimator_checks import check_estimator def test () : me = MyEstimator(**params) check_estimator(me)
If I run the test, I get the following error message :
AssertionError: The error message should contain one of the following patterns: 0 feature\(s\) \(shape=\(\d*, 0\)\) while a minimum of \d* is required.
I don't understand how I am supposed to take care of that. I am even more surprise because my fit_transorm method uses self._validate_data at the beginning. I would expect that function to take care of case like these. Could someone help me with that issue ?
[FEATURE REQUEST] Add GitHub Organisation README profile
Just found out this new GitHub feature on GitHub org.
Is cohen kappa score and balanced accuracy score supposed to work w/ multiclass labels?
I have a 3-class classification and I'm trying to use
cross_validate, but it returns nans for all my scores. I tested the problem by running
cross_val_score on all scores individually and isolated it to those 2 metrics.
X = (100, 5)
y = (100, 3)
clf is a Random Forest Classifier
from sklearn.model_selection import cross_val_score cross_val_score(clf, X, y, cv=5, scoring='balanced_accuracy')
@razou could you please paste a fully reproducible piece of code?
from sklearn.calibration import CalibratedClassifierCV from sklearn.multioutput import ClassifierChain from lightgbm import LGBMClassifier base_estimator = LGBMClassifier() calibrator = CalibratedClassifierCV(base_estimator=base_estimator) clf = ClassifierChain(base_estimator=calibrator, order='random', random_state=20) clf.fit(X=train_x, Y=train_y) y_pred_proba = clf.predict_proba(validation_x)
train_ywhich is probably the core of the problem. Using minimal random data from np.random.normal(size=(n_samples, n_features) or np.random.randint(low=0, high=10, size=n_samples)