logistic regression.

logistic regression.

logistic regression with a

Let's start talking about logistic regression.

light of logistic regression, the Support

logistic regression and we're seeing what the mathematical formula is defining the hypothesis for logistic regression.

Today, we'll see how multiple regression, multiple regression works where we have multiple predictors.

And then, we can have multiple predictors and multiple regression coefficients.

We, just ran a multiple regression analysis.

So, for logistic regression, we had two terms to our objective function.

And what's new is doing multiple regression analysis, asking for standardized regression coefficients.

How do we do that in multiple regression?

Particularly when we do things like multiple regression.

That's the beauty of doing multiple regression analysis.

We're just gonna do a multiple regression analysis.

At first we've covered multiple regression analysis sort of conceptually, and how to interpret regression coefficients when there are multiple predictors in our regression equation.

We already have linear regression and we have logistic regression, so why do we need, you know, neural networks?

Now let's think about what we will like the logistic regression to do.

Could we use Naive Bayes, k Nearest Neighbors, Support Vector Machines, Logistic Regression.

You can do a multiple regression analysis by hand.

Well, let's see that in a multiple regression analysis.

logistic regression, I'm going to modify this a little bit, and make the hypothesis

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

And today, I'd like to introduce you to multiple regression.

So, I'll just summarize the multiple regression equation as follows.

Then, we can apply that to the multiple regression example.

To see how we get those. Multiple, regression coefficients estimated.

But first lets just start with basic multiple regression analysis.

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

A plus B. So for logistic regression if we send a very large value of

So, simple logistic regression together with adding in maybe the quadratic or the cubic features

In the next video we'll start working out the details of the logistic regression algorithm.

So the main topic of this segment is just again estimation of regression coefficients in multiple regression.

And today we're going to do multiple regression analysis in R.

You'll have to write a script that does multiple regression analysis.

For such situations there is a model called logistic regression, which uses a slightly more complicated model than linear regression, which goes as follows .

That's the term that a single training example contributes to the overall objective function for logistic regression.

If you want to apply logistic regression to this problem, one thing you could do is apply

Regression co efficients, in a multiple regression, using matrix algebra and last week, you didn't even know matrix algebra.

So in the first segment, we'll run a first multiple regression analysis.

last segment demonstrating the use of multiple regression analysis to test mediation.

So, a better approach would be to have multiple models, multiple regression models, and test them against each other.

And that's called the support vector machine, and compared to both the logistic regression and neural networks, the

line similar to logistic regression but this is going to be the straight line in this posh inning.

Where instead of having basically just one logistic regression output unit, we may instead have K of them.