ML Terminology

Predictions.

y is the label we want to predict a is the slope of the line x are the input values b is the intercept

y is the label we want to predict w is the weight (the slope) x are the features (input values) b is the intercept

In Machine Learning terminology, the label is the thing we want to predict.

in a linear graph:

In Machine Learning terminology, the features are the input.

values in a linear graph:

Sometimes there can be many features (input values) with different weights:

x

+ w

x

+ w

x

+ w

x

A Model defines the relationship between the label (y) and the features (x). There are three phases in the life of a model:

The goal of training is to create a model that can answer a question. Like what is the expected price for a house?

Inference is when the trained model is used to infer (predict) values using live data. Like putting the model into production.