Classification

Get your carcass out of here!

Is that an animal carcass behind her?

I think that's an animal carcass behind her.

linear regression for classification problems.

For classification, we know that

Earlier, we talked about emails, spam classification as an example of a classification problem.

What else? Do you want to move into a carcass?

This is the guys on the rope, pulling open the carcass.

You can't sweep that carcass under the rug. Who bought you?

Here are some examples of classification problems.

Today we'll talk about classification versus regression.

Count Dooku is paying me a fortune to deliver your slimy carcass.

Tagging is a cooperative infrastructure answer to classification.

Our example so far was one of classification.

Regression problems are fundamentally different from classification problems.

Let's say we're using the tumor classification example.

So, how do we develop a classification algorithm?

The closest classification it matches is a virus.

In HarvardYenching, it's a classification for Asian literature.

The second type of classification problem we'll consider will be multiclass classification problem where we may have k distinct classes.

Skipping ahead a bit more here, this is Rony on the whale carcass.

We're going to consider two types of classification problems.

Now, in 1970s, Bautz and Morgan introduced a different classification.

That we could create a classification of the mental illnesses.

We're testing everyone with a level four classification or above.

Here he envisions the carcass of an autorickshaw he once witnessed burning in a riot.

I gave up my life so you could drag that worthless carcass around and call it living?

But what you find is this is different from classification as before.

Support vector machines are supervised learning models used for classification and regression analysis.

This is from the classification case, except this time the Ys are continuous.

Here's our linear separation classification function where this is our common linear function.

I'm going to focus on the application of neural networks to classification problems.

We talked about classification and regression and different methods to do supervised learning.

Let's now do classification and see what class and message it ends up with.

You can toss them easily into the naive base model and get better classification.

If this is larger or equal to zero, then we call our classification 1.

Consider a supervised learning classification problem where you have a training set like this.

Logistic Regression is actually a classification algorithm that we apply to settings where the

The first is binary classification, where the labels y are either zero or one.

To introduce a bit more terminology this is an example of a classification problem.

How about if we were doing a classification problem and say using logistic regression instead.

So, applying linear regression to a classification problem usually isn't, often isn't a great idea.

This is called a multi class classification problem, but for the next few videos, let's start with the two class or the binary classification problem. and we'll worry about the multi class setting later.

And so it's anachronistically a classification by fathers' occupations, so single parents go on their own.