SCIKIT-LEARN DESIGN
阿新 • • 發佈：2018-12-09
- Estimators. Any object that can estimate some parameters based on a dataset is called an estimator (e.g., an imputer is an estimator). The estimation itself is performed by the fit() method, and it takes only a dataset as a parameter (or two for supervised learning algorithms; the second dataset contains the labels). Any other parameter needed to guide the estimation process is considered a hyperparameter (such as an imputer ’s strategy ), and it must be set as an instance variable (generally via a constructor parameter).
- Transformers. Some estimators (such as an imputer ) can also transform a dataset; these are called transformers. Once again, the API is quite simple: the transformation is performed by the transform() method with the dataset to transform as a parameter. It returns the transformed dataset. This transformation generally relies on the learned parameters, as is the case for an imputer . All transformers also have a convenience method called fit_transform() that is equivalent to calling fit() and then transform() (but sometimes fit_transform() is optimized and runs much faster).
- Predictors. Finally, some estimators are capable of making predictions given a dataset; they are called predictors. A predictor has a predict() method that takes a dataset of new instances and returns a dataset of corresponding predictions. It also has a score() method that measures the quality of the predictions given a test set (and the corresponding labels in the case of supervised learning algorithms).