11 key concepts of Machine Learning.<br/><br/>— Supervised …

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1. Labels

(Also referred to as "y")

The label is the piece of information that we are predicting.

For example:

- the animal that's shown in a picture
- the price of a house
- whether a message is spam or not

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2. Features

(Also referred to as "x")

These are the input variables to our problem. We use these features to predict the "label."

For example:

- pixels of a picture
- number of bedrooms of a house
- square footage of a house

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3. Samples

(This is also known as "examples.")

A sample is a particular instance of data (features or "x.") It could be "labeled" or "unlabeled."

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4. Labeled sample

Labeled samples are used to train and validate the model. These are usually represented as (x, y), where "x" is a vector containing all the features, and "y" is the corresponding label.

For example, a labeled sample could be:

([3, 2, 1500], 350000)

5. Unlabeled sample

Unlabeled samples contain features, but they don't contain the label: (x, ?)

We usually use a model to predict the labels of unlabeled samples.

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6. Model

A model defines the relationship between features and the label.

You can think of a model as a set of rules that, given certain features, determines the corresponding label.

For example, given the # of bedrooms, bathrooms, and square footage, we get the price.

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7. Training

Training is a process that builds a model.

We show the model labeled samples during training and allow the model to gradually learn the relationships between features and the label.

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8. Validation

Validation is the process that lets us know whether a model is any good.

Usually, we run a set of (unseen) labeled samples through a model to ensure that it can predict the labels.

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9. Inference

Inference is the process of applying a trained model to unlabeled samples to obtain the corresponding labels.

In other words, "inference" is the process of making predictions using a model.

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10. Regression

A regression model predicts continuous values, for example:

- the value of a house
- the price of a stock
- tomorrow's temperature

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11. Classification

A classification model predicts discrete values, for example:

- the picture is showing a dog or a cat
- the message is spam or not
- the forecast is sunny or overcast

11 key concepts of Machine Learning.

— Supervised Learning Edition —

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11 key concepts of Machine Learning.

— Supervised Learning Edition —

🧵👇