linear regression for classification problems.

For classification, we know that

Classification

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

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

Here are some examples of classification problems.

Today we'll talk about classification versus regression.

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

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.

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

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

For now, we're going to start with classification problems with just two classes zero and one.

We've seen classification before for spam and other types of categories, language ID and so on.

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.

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.

So, for each of these problems, should they be treated as a classification problem, or as a regression problem?

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

We so far have studied linear functions for regression, but linear functions are also used for classification, and specifically for an algorithm called the perceptron algorithm.

And it turns out that in classification problems sometimes you can have more than two values for the two possible values for the output.

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.

Here is one other funny thing about what would happen if we were to use linear regression for a classification problem.

Which of these would be a good algorithm or technique for doing this classification into things like people, places, and drugs.

In classification the output is binary or a fixed number of classes for example something is either a chair or not.

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.

English, German, French, Spanish, and Azerbaijani and tell me for each of these want your best guess is at the most likely language classification.

I can take around with me whenever I go, and this is called Animalia Chordata, from the Latin nomenclature for human being, classification system.

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