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ML Terminology

Key Machine Learning Terminologies are:

  • Relationships
  • Labels
  • Features
  • Models
  • Training
  • Inference

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:

Algebra Machine Learning
y = ax + b y = b + wx

Machine Learning Features

In Machine Learning terminology, the features are the input.

They are like the x values in a linear graph:

Algebra Machine Learning
y = ax + b y = b + wx

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.