Overview of Classification Methods in Python with Scikit-Learn
Introduction
Are you a Python programmer looking to get into machine learning? An excellent place to start your journey is by getting acquainted with Scikit-Learn.
Doing some classification with Scikit-Learn is a straightforward and simple way to start applying what you’ve learned, to make machine learning concepts concrete by implementing them with a user-friendly, well-documented, and robust library.
What is Scikit-Learn?
Scikit-Learn is a library for Python that was first developed by David Cournapeau in 2007. It contains a range of useful algorithms that can easily be implemented and tweaked for the purposes of classification and other machine learning tasks.
Scikit-Learn uses SciPy as a foundation, so this base stack of libraries must be installed before Scikit-Learn can be utilized.
Defining our Terms
Before we go any further into our exploration of Scikit-Learn, let’s take a minute to define our terms. It is important to have an understanding of the vocabulary that will be used when describing Scikit-Learn’s functions.
To begin with, a machine learning system or network takes inputs and outputs. The inputs into the machine learning framework are often referred to as “features”