Learning Data Mining with Python

Classifying with scikit-learn Estimators
Most scikit-learn estimators use the NumPy arrays or a related format for input
and output.
There are a large number of estimators in scikit-learn. These include support vector
machines (SVM), random forests, and neural networks. Many of these algorithms
will be used in later chapters. In this chapter, we will use a different estimator from
scikit-learn: nearest neighbor.
For this chapter, you will need to install a new library called
matplotlib. The easiest way to install it is to use pip3, as you did in
Chapter 1, Getting Started with Data Mining, to install scikit-learn:
$pip3 install matplotlib
If you have any difficulty installing matplotlib, seek the official
installation instructions at http://matplotlib.org/users/
installing.html.
Nearest neighbors
Nearest neighbors is perhaps one of the most intuitive algorithms in the set of
standard data mining algorithms. To predict the class of a new sample, we look
through the training dataset for the samples that are most similar to our new sample.
We take the most similar sample and predict the class that the majority of those
samples have.
As an example, we wish to predict the class of the triangle, based on which class it is
more similar to (represented here by having similar objects closer together). We seek
the three nearest neighbors, which are two diamonds and one square. There are more
diamonds than circles, and the predicted class for the triangle is, therefore, a diamond:
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