Okay, in this course, we will see a law for this matter here, so let's say we create any of the linear regression model the neural network model or the naive Bayes model. So, we will usually put this on here. So what this means is that he, I have a data set Okay, let's see if I have a dataset Iris data set here, then I have a few variables. Then I want to what this formula means is, I say I want to predict a species variable. Then I can write our species then This sign here then this dot here means all the other variables. So, what this means is that I want to predict this species variable.

This dot means all of the variables that means our predict species variable using all other variables. So, I can also write something like SEPA then Ross, pet v. So, I will only use sepal and petal way to predict these species here. So, if I say I put dog here, that means, I will be using all the variables in the data set to predict these species. So, in a data set something IDs I can separate sepal width, petal length, petal width and a species. So we have a dot here, I will be using all these variables to predict this species species variable. A value something is set by land brass pet plan.

Now we'll only be using sepal and petal land to predict this species. So this is what is our formula here me. So let's say after I trained a model, and I pre did a test set, eventually I want to make Ah, I want to make a test nicer. Let's see I have a prediction result here. I can pull this prediction result into this test set. I can do something like this Test say predict to something like this you can put equal so, I can view the whole data set I can do something like this.

So either a pen or a prediction result into the test set. Okay, so in my test, these are species here, and this is a pre data pre data species. So I can append a prediction result into the test set by writing something like this