Original subtitles

So supervised learning algorithms learn to predict input, output or X to Y mapping.

And in the last video you saw that regression algorithms,

which is a type of supervised learning algorithm learns to predict numbers out

of infinitely many possible numbers.

There's a second major type of supervised learning algorithm called a classification

algorithm.

Let's take a look at what this means.

Take breast cancer detection as an example of a classification problem.

Say you're building a machine learning system so

that doctors can have a diagnostic tool to detect breast cancer.

This is important because early detection could potentially save a patient's life.

Using a patient's medical records your machine learning system tries to

figure out if a tumor that is a lump is malignant meaning cancerous or dangerous.

Or if that tumor, that lump is benign, meaning that it's just

a lump that isn't cancerous and isn't that dangerous?

Some of my friends have actually been working on this specific problem.

So maybe your dataset has tumors of various sizes.

And these tumors are labeled as either benign,

which I will designate in this example with a 0 or

malignant, which will designate in this example with a 1.

You can then plot your data on a graph like this where

the horizontal axis represents the size of the tumor and

the vertical axis takes on only two values 0 or

1 depending on whether the tumor is benign, 0 or malignant 1.

One reason that this is different from regression is that we're trying to predict

only a small number of possible outputs or categories.

In this case two possible

outputs 0 or 1,

benign or malignant.

This is different from regression which tries to predict any number,

all of the infinitely many number of possible numbers.

And so the fact that there are only two possible outputs is

what makes this classification.

Because there are only two possible outputs or

two possible categories in this example,

you can also plot this data set on a line like this.

Right now, I'm going to use two different symbols to denote

the category using a circle an O to denote the benign examples and

a cross to denote the malignant examples.

And if new patients walks in for a diagnosis and

they have a lump that is this size, then the question is,

will your system classify this tumor as benign or malignant?

It turns out that in classification problems you can also have more than two

possible output categories.

Maybe you're learning algorithm can output multiple types of cancer

diagnosis if it turns out to be malignant.

So let's call two different types of cancer type 1 and type 2.

In this case the average would have three possible output

categories it could predict.

And by the way in classification, the terms output classes and

output categories are often used interchangeably.

So what I say class or category when referring to the output,

it means the same thing.

So to summarize classification algorithms predict categories.

Categories don't have to be numbers.

It could be non numeric for example,

it can predict whether a picture is that of a cat or a dog.

And it can predict if a tumor is benign or malignant.

Categories can also be numbers like 0, 1 or 0, 1, 2.

But what makes classification different from regression when

you're interpreting the numbers is that classification predicts

a small finite limited set of possible output categories such as 0, 1 and

2 but not all possible numbers in between like 0.5 or 1.7.

In the example of supervised learning that we've been looking at,

we had only one input value the size of the tumor.

But you can also use more than one input value to predict an output.

Here's an example, instead of just knowing the tumor size,

say you also have each patient's age in years.

Your new data set now has two inputs, age and tumor size.

What in this new dataset we're going to use circles to show patients whose tumors

are benign and crosses to show the patients with a tumor that was malignant.

So when a new patient comes in, the doctor can measure the patient's tumor size and

also record the patient's age.

And so given this,

how can we predict if this patient's tumor is benign or malignant?

Well, given the day said like this, what the learning algorithm might do

is find some boundary that separates out the malignant tumors from the benign ones.

So the learning algorithm has to decide how to fit a boundary line

through this data.

The boundary line found by the learning algorithm would help the doctor with

the diagnosis.

In this case the tumor is more likely to be benign.

From this example we have seen how to inputs the patient's age and

tumor size can be used.

In other machine learning problems often many more input values are required.

My friends who worked on breast cancer detection use many additional inputs,

like the thickness of the tumor clump, uniformity of the cell size,

uniformity of the cell shape and so on.

So to recap supervised learning maps input x to output y,

where the learning algorithm learns from the quote right answers.

The two major types of supervised learning our regression and classification.

In a regression application like predicting prices of houses, the learning

algorithm has to predict numbers from infinitely many possible output numbers.

Whereas in classification the learning algorithm has to make a prediction of

a category, all of a small set of possible outputs.

So you now know what is supervised learning,

including both regression and classification.

I hope you're having fun.

Next there's a second major type of machine learning

called unsupervised learning.

Let's go on to the next video to see what that is