# ML Terminology

• Relationships
• Labels
• Features

## Machine Learning 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
• Machine Learning Training
• Machine Learning Inference
• Machine Learning Phases

## 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.

## Machine Learning Phases

Machine learning has two main phases:

1. Training:
Input data are used to calculate the parameters of the model.

2. Inference:
The "trained" model outputs correct data from any input.

• Supervised Machine Learning
• Unsupervised Machine Learning
• Self-Supervised Machine Learning

## Supervised Learning

Supervised Machine Learning uses a set of input variables to predict the value of an output variable.

Supervised learning uses labeled data (data with known answers) to train algorithms to:

• Classify Data
• Predict Outcomes

Supervised learning can classify data like "What is spam in an e-mail", based on known spam examples.

Supervised learning can predict outcomes like predicting what kind of video you like, based on the videos you have played.

## Unsupervised Learning

Unsupervised Machine Learning uses patterns from any unlabeled dataset, trying to understand patterns (or groupings) in the data.

Unsupervised learning is used to predict undefined relationships like meaningful patterns in data.

It is about creating computer algorithms than can improve themselves.

It is expected that machine learning will shift to unsupervised learning to allow programmers to solve problems without creating models.

## Reinforcement Learning

Reinforcement learning is based on non-supervised learning but receives feedback from the user whether the decision is good or bad. The feedback contributes to improving the model.

## Self-Supervised Learning

Self-supervised learning is similar to unsupervised learning because it works with data without human added labels.

The difference is that unsupervised learning uses clustering, grouping, and dimensionality reduction, while self-supervised learning draw its own conclusions for regression and classification tasks.

×

## Contact Sales

If you want to use W3Schools services as an educational institution, team or enterprise, send us an e-mail:
sales@w3schools.com