Part of list:

Tutorial: Linearly separable data

- Algebraic definition:

Tutorial: Linearly separable data[ Edit ]

The idea of linearly separable is easiest to visualize and understand in 2 dimensions. Let the two classes be represented by colors red and green.

A dataset is said to be linearly separable if it is possible to draw a line that can separate the red and green points from each other.

Here are same examples of **linearly separable data**:

And here are some **examples of linearly non-separable data**

This concept can be extended to three or more dimensions as well. For example, below is an **example of a three dimensional dataset that is linearly separable**

**In n dimensions, the separator is a (n-1) dimensional hyperplane - **although it is pretty much impossible to visualize for 4 or more dimensions.

Algebraically, the separator is a linear function, i.e. if data point x is given by (x1, x2), when the separator is a function f(x) = w1*x1 + w2*x2 + b

All points for which f(x) = 0, are on the separator line. All points for which f(x) > 0 are on one side of the line, and all points for which f(x) < 0 are on the other side.

Read more…(192 words)

Mark as completed

Also part of lists:

Previous

Perceptron algorithm

Next

Quiz: Perceptron algorithm

About the contributor:

Kavita RawatMachine Learning and Deep Learning enthusiast

100%

Loading…

Have a question? Ask here…

Post

Part of list:

Tutorial: Linearly separable data

- Algebraic definition:

Contributor

Kavita RawatMachine Learning and Deep Learning enthusiast

100%

Ready to join our community?

Sign up below to automatically get notified of new lists, get **reminders** to finish ones you subscribe to, and **bookmark** articles to read later.

Continue with Facebook

— OR —

Your Full Name

Email address

I have an account. Log in instead

By signing up, you agree to our Terms and our Privacy Policy.