Classification

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

Here are some examples of classification problems.

Our example so far was one of classification.

linear regression for classification problems.

For classification, we know that

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

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

That we could create a classification of the mental illnesses.

Today we'll talk about classification versus regression.

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

Tagging is a cooperative infrastructure answer to 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?

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

But when people talk about taxonomy and in particular, in Carl Linnaeus' case they're talking about the classification of living things, so classifying organisms.

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

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

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

It believes in classification of human beings, humiliation of other nations, trampling upon the rights of others and their domination.

And, and we can do that for each detector and essentially this gives us a handle on how to come up with the classifications of more finding classification of people.

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

Some Swedes very kindly classified a lot of their infant deaths according to the British register of general socioeconomic classification.

And because there's a small number of discrete values, I would therefore treat it as a classification problem.

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.

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

levels of trust, obesity, mental illness which in standard diagnostic classification includes drug and alcohol addiction and social mobility.

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

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.

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.

Just generic classification which there are some approaches which can do a good job with, just maybe some assembly of features.

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

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

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

And we talked about the classification problem, where the goal is to predict a discrete value output.