So, now that we have defined the training validation or cross validation and test sets, we can also define the training error, cross validation error, and test error.

Validation

So, that was model selection and how you can take your data and split it into a train validation and test set, and use your cross validation data to select model and evaluate it on the test set.

Remote Validation

Local Validation

Validation Report

Internal Validation

Validation Options

XML Validation

Again, validation.

CSS and HTML validation tools

And similarly, I'm then also going to define the cross validation set error when the test set error, as before to be the average sum of squared errors on the cross validation and the test sets.

cross validation, and I am going to abbreviate cross validation as CV.

Accessibility validation level

Script Validation Failed

Online Certificate Validation

Test are just the average squared error, or one half of the average squared error on my training validation and test sets without the extra regularization chart.

Sometimes it's also called the validation set, instead of cross validation set.

Validation for a cell failed

And, finally, test and test it again.

Just select the training error, except measure it on the cross validation data set, and here's my test set error, same as before.

I'm going to abbreviate cross validation

Now that we're armed with the notion of training and validation in test sets, we can understand the concepts of bias and variance a little bit better.

Test test.

learning algorithm and it is not performing as well as your are hoping, so your cross validation set error or your test set error is high.

So when theta with the model selection problem like this is, instead of using the test set to select the model, we're instead going to use validation set or the cross validation set to select the model.

And we'll give you a test. And the test is tomorrow.

Next, let's look at the cross validation error.

And in our discussion of how to do this we'll talk about not just how to split your data into a train and test sets but how to split your data into what we'll discover is called the train validation and test sets.

Mic test, mic test.

And so our training set will now be only, maybe 60 of the data, and our cross validation set or our validation set will have some number of examples.

We'll call print_all_links with our test string that has test 1, test 2, and test 3 as the 3 links.

The same form and the same validation. You've seen all this code before.

Enable spell checking for validation of the OCR result

XiCV, YiCV to denote the i cross validation example.

Otherwise we continue on and we set a size and a current test, best size and best test to be the length of the current test and test itself.

I'll just warn you that just sometimes people do you know, use the same data for the purpose of the validation set and for the purpose of the test sets.

Test

Test

Test...

Test

Test

And so, this is what the cross validation error and what the training error may look

Let's look at the training error and cause validation error and plot them on this figure.

We won't be doing well on the cross validation set.