# 5 minutes of Machine Learning: Basic Definitions [Day 2]

For those who are starting here… Consume these articles (Titled “5 minutes of Machine Learning”) for the bottom line up front takeaways I would like to share as I take Google’s Machine Learning Crash Course.

**Now, let’s start with some definitions that you probably knew OF, but didn’t know about in a mathematical context:**

**Labels:** labels are the targets we are trying to predict, also the labels you use for… well, labeling data. This is expressed as (y).

**Features: **features are input variables that describe our data. The first dimension is typically expressed as x, with the subscript numbering [1, 2, etc…] refer to the different values that go into that dimension.

*Example: *Lets take dimensions of what makes spam email really annoying. Here are your dimensions:

- words in the email text
- sender’s address
- time of day the email was sent
- email contains the phrase “one weird trick.”

**A labeled example: **one piece of data with a feature and accompanying label. Mathematically, in the format of: `{features, label}: (x, y).`

Labeled examples are used to train the actual model.

On the other hand, **unlabeled examples** are used to make predictions on new data, in the format of `{features, label}: (x, ?)`

**A model defines the relationship between features and label**.

**Model training** is when you show the model labeled examples and enable the model to gradually learn the relationships between features and label.

**Inference** occurs when you expose the trained model to unlabeled examples.

**REGRESSION vs. CLASSIFICATION**

Initially… the two terms had me baffled. Classification… like object classification? Yes, Amina.

Regression and classification are two different approaches to prediction and/or inference.

**Regression [simplified] predicts continuous values** and answers questions like: What is the value of farmland in Iowa? Better yet, what will it be in x years? When should I finally buy that cattle farm I have always wanted?

**Classification [simplified] predicts discrete values. **If you have read my series on building an object classification model, this is the part where you ask the model: is it a car? truck? semi? van? See! Discrete values. Classification models typically learn to distinguish between two or more discrete classes.

That is all for definitions today. Stay tuned for the next article on line fitting.