sklearn.metrics Module

An sklearn.metrics Module is an sklearn module that contains a collection of Metrics subroutines.

• Context:
  • It can (often) reference a sklearn.metrics system.
    • sklearn.metrics.Metric_Name(self, arguments) or simply sklearn.metrics.Metric_Name()

      where Metric_Name is the name of the selected Metric subroutine.

  • It can range from being Classification Metrics, to being Regression Metrics, to being Multilabel Ranking Metrics, to being Clustering Metrics, to being Biclustering Metrics, to being Pairwise Metrics.
• Example(s)
  • sklearn.metrics.pairwise Submodule.
  • sklearn.metrics Classification Submodule.
  • sklearn.metrics Regression Submodule.
  • sklearn.metrics Multilabel Ranking Submodule.
  • sklearn.metrics Biclustering Submodule.
  • sklearn.metrics.cluster Submodule.
• Counter-Example(s):
  • sklearn.manifold, a collection of Manifold Learning Systems.
  • sklearn.tree, a collection of Decision Tree Learning Systems.
  • sklearn.ensemble, a collection of Decision Tree Ensemble Learning Systems.
  • sklearn.covariance,a collection of Covariance Estimators.
  • sklearn.cluster.bicluster, a collection of Spectral Biclustering Algorithms.
  • sklearn.linear_model, a collection of Linear Model Regression Systems.
  • sklearn.neighbors, a collection of K Nearest Neighbors Algorithms.
  • sklearn.neural_network, a collection of Neural Network Systems.
• See: Metric, Pairwise Distance, Clustering, Regression Analysis Task, Classification Task, MSE, RMSE.

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References

2017A

• (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics Retrieved:2017-11-12
  • QUOTE: See the Model evaluation: quantifying the quality of predictions section and the Pairwise metrics, Affinities and Kernels section of the user guide for further details.

    The sklearn.metrics module includes score functions, performance metrics and pairwise metrics and distance computations.

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