Train and Evaluate Regression Models (2/4)

Previous << Train and Evaluate Regression Models (1/4)

Supervised machine learning techniques involve training a model to operate on a set of features and predict a label using a dataset that includes some already-known label values.

The training process fits the features to the known labels to define a general function that can be applied to new features for which the labels are unknown and predict them. You can think of this function like this, in which y represents the label we want to predict, and x represents the features the model uses to predict it: y = f(x)

In most cases, x is actually a vector that consists of multiple feature values, so to be a little more precise, the function could be expressed like this:
y = f([x1​, x2​, x3​, …])

The goal of training the model is to find a function that performs some kind of calculation to the x values that produces the result y. We do this by applying a machine learning algorithm that tries to fit the x values to a calculation that produces y reasonably accurately for all of the cases in the training dataset.

There are many machine learning algorithms for supervised learning, and we can broadly divide them into two types:

• Regression algorithms: Algorithms that predict a y value that is a numeric value, such as the price of a house or the number of sales transactions.
• Classification algorithms: Algorithms that…