2013/7/26 Andreas Mueller <[email protected]>:
> If you are ok with relying on scikit-learn, you can inherit from
> BaseEstimator and ClassifierMixin, then implement fit, predict and
> __init__ (to set the parameters).
We could add some code skeletons to that, like
class MajorityClassifier(BaseEstimator, ClassifierMixin):
"""Predicts the majority class of its training data."""
def __init__(self):
pass
def fit(self, X, y):
self.classes_, indices = np.unique(["foo", "bar", "foo"],
return_inverse=True)
self.majority_ = np.argmax(np.bincount(indices))
def predict(self, X):
return np.repeat(self.classes_[self.majority_], len(X))
--
Lars Buitinck
Scientific programmer, ILPS
University of Amsterdam
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