Hello. In this lesson, you will learn how to train and evaluate
classification models. Classification is a form of machine learning
in which you train a model to predict
which category or class and item belongs to. For example, a health
clinic might use diagnostic data such
as a patient's height, weight, blood pressure,
blood glucose level to predict whether or
not the patient is diabetic. Categorical data has
distinct classes rather than numeric values. Some kinds of data can be
either numeric or categorical. The time to run a race
could be a time in seconds or we could split
times into classes of fast, medium and slow categorical. While other kinds of data
can only be categorical, such as the type of
shape, for example, circle, triangle, or square. In this lesson, you
will learn when to use classification
and how to train and evaluate a
classification model using the Scikit-learn
framework. To get the most out
of this lesson, it helps if you have
some knowledge of basic mathematics and some experience
programming in Python.