Supervised learning
  • References/Python/scikit-learn/Tutorials

The problem solved in supervised learning

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An introduction to machine learning with scikit-learn
  • References/Python/scikit-learn/Tutorials

Section contents In this section, we introduce the

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Putting it all together
  • References/Python/scikit-learn/Tutorials

Pipelining We have seen that some estimators can transform data and that some estimators can predict variables. We can also create combined estimators:

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A tutorial on statistical-learning for scientific data processing
  • References/Python/scikit-learn/Tutorials

Statistical learning

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Choosing the right estimator
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Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different

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Unsupervised learning
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Clustering: grouping observations together The problem solved in clustering Given the

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Statistical learning
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Datasets Scikit-learn deals with learning information from one or more datasets that are represented as 2D arrays. They can be understood as a list of multi-dimensional observations

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Model selection
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Score, and cross-validated scores As we have seen, every estimator exposes a score method that can judge the

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Working With Text Data
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The goal of this guide is to explore some of the main scikit-learn tools on a single practical task: analysing a collection of text documents (newsgroups

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scikit-learn Tutorials
  • References/Python/scikit-learn/Tutorials

An introduction to machine learning

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