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Classifying with scikit-learn
Estimators
The scikit-learn library is a collection of data mining algorithms, written in Python
and using a common programming interface. This allows users to easily try different
algorithms as well as utilize standard tools for doing effective testing and parameter
searching. There are a large number of algorithms and utilities in scikit-learn.
In this chapter, we focus on setting up a good framework for running data mining
procedures. This will be used in later chapters, which are all focused on applications
and techniques to use in those situations.
The key concepts introduced in this chapter are as follows:
• Estimators: This is to perform classification, clustering, and regression
• Transformers: This is to perform preprocessing and data alterations
• Pipelines: This is to put together your workflow into a replicable format
scikit-learn estimators
Estimators are scikit-learn's abstraction, allowing for the standardized
implementation of a large number of classification algorithms. Estimators are used
for classification. Estimators have the following two main functions:
• fit(): This performs the training of the algorithm and sets internal
parameters. It takes two inputs, the training sample dataset and the
corresponding classes for those samples.
• predict(): This predicts the class of the testing samples that is given as
input. This function returns an array with the predictions of each input
testing sample.
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