ML Terminology

Relationships

Machine learning systems uses Relationships between Inputs to produce Predictions.

In algebra, a relationship is often written as y = ax + b:

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

With ML, a relationship is written as y = b + wx:

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

Machine Learning Labels

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

It is like the y in a linear graph:

Machine Learning Features

In Machine Learning terminology, the features are the input.

They are like the x values in a linear graph:

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

y = b + w₁x₁ + w₂x₂ + w₃x₃ + w₄x₄

Machine Learning Models

A Model defines the relationship between the label (y) and the features (x).

There are three phases in the life of a model:

• Data Collection
• Training
• Inference

Machine Learning Training

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

Machine Learning Inference

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