scikit-learn chinese document-model evaluation-unsupervised learning | apachcn
Published: 2019-05-15

chinese document: http://sklearn.Apache cn.org/cn/stable/modules/model _ evaluation.html

english document: http://sklearn.Apache cn.org/en/stable/modules/model _ evaluation.html

Official Document: http://scikit-learn.org/stable/

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3.3. Model Evaluation: Quantifying the Quality of Forecast

There are 3 different API to evaluate the quality of model predictions:

  • Estimator score method: Estimators have a score method that provides the default for the problems they solve.
    Evaluation criterion.There are no related discussions on this page, but there are related discussions in each estimator document.
  • Scoring parameter: Model-evaluation tools uses  cross-validation  (such as model _ selection.cross _ val _ score and  model_selection.GridSearchCV)
    Rely on internal  scoring strategy.This is in  scoring.
    Parameters: Discussion on Defining Model Evaluation Rules.
  • Metric functions:  metrics  module realizes the function of evaluating prediction errors for specific purposes.These indexes include classification index, multi-label ranking index, regression index and clustering index.

Finally, virtual estimation is used to obtain the benchmark values of these indexes for random prediction.




chinese document: http://sklearn.Apache cn.org/cn/stable/modules/model _ evaluation.html

english document: http://sklearn.Apache cn.org/en/stable/modules/model _ evaluation.html

Official Document: http://scikit-learn.org/stable/

github: https://github.com/apachecn/scikit-learn-doc-zh
Star, we have been working hard)

Contributor: https://github.com/apachecn/scikit-learn-doc-zh# Contributor

About Us: http://www.apachecn.org/organization/209.html

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