DOC clearer doc in contributing guidelines
@@ -916,41 +916,41 @@ multiple interfaces):
The base object, implements a ``fit`` method to learn from data, either::
- estimator = obj.fit(data, targets)
+ estimator = estimator.fit(data, targets)
or::
- estimator = obj.fit(data)
+ estimator = estimator.fit(data)
:Predictor:
For supervised learning, or some unsupervised problems, implements::
- prediction = obj.predict(data)
+ prediction = predictor.predict(data)
Classification algorithms usually also offer a way to quantify certainty
of a prediction, either using ``decision_function`` or ``predict_proba``::
- probability = obj.predict_proba(data)
+ probability = predictor.predict_proba(data)
:Transformer:
For filtering or modifying the data, in a supervised or unsupervised
way, implements::
- new_data = obj.transform(data)
+ new_data = transformer.transform(data)
When fitting and transforming can be performed much more efficiently
together than separately, implements::
- new_data = obj.fit_transform(data)
+ new_data = transformer.fit_transform(data)
:Model:
A model that can give a `goodness of fit <https://en.wikipedia.org/wiki/Goodness_of_fit>`_
measure or a likelihood of unseen data, implements (higher is better)::
- score = obj.score(data)
+ score = model.score(data)
Estimators
----------
