So 0 may denote the benign tumor and 1 positive class may denote a malignant tumor.

If someone discovers a breast tumor, a lump in their breast, a malignant tumor is a tumor that is harmful and dangerous and a benign tumor is a tumor that is harmless.

Now, in this example we use only one feature or one attribute, mainly, the tumor size in order to predict whether the tumor is malignant or benign.

The machine learning question is, can you estimate what is the probability, what is the chance that a tumor is malignant versus benign?

So here is a nine year old boxer named Milo, who had a very aggressive tumor called a malignant neurofibroma growing on his shoulder.

In other words, I'm going to tell my patient that the tumor, sadly, has a 70 chance or a 0.7 chance of being malignant.

likely to be benign than malignant. In this example we had two features, namely, the age of the patient and the size of the tumor.

We can revert the malignant phenotype.

And from the evil of malignant witchcraft,

So here's a tumor.

The tumor in your leg is not consistent with a benign tumor.

Back then, recall data sets that look like this, where each example was labeled either as a positive or negative example, whether it was a benign or a malignant tumor.

Tom had a brain tumor.

You don't have a tumor.

learning problem is called the classification problem where we predict discrete valued outputs such as if we are looking at cancer tumors and trying to decide if a tumor is malignant or benign.

And in order to show you that the malignant phenotype

We think the entire tumor is out.

Can you tell where the tumor is?

Uniformity of cell size of the tumor.

In this case, a one centimeter tumor.

Probably a 24hour tumor. They're going around.

Here's an example of a training set for a classification task for classifying a tumor as malignant or benign and notice that malignancy takes on only two values zero or no or one or one or yes.

Let's say we're using the tumor classification example.

Maybe even find a tumor, in some cases.

Maybe even find a tumor, in some cases.

Tumor cells can travel through the blood vessels.

like, you know, breast cancer, is, zero is benign, one is malignant.

Let's see a collected data set and suppose in your data set you have on your horizontal axis the size of the tumor and on the vertical axis I'm going to plot one or zero, yes or no, whether or not these are examples of tumors we've seen before are malignant which is one or zero if not malignant or benign.

What about the nerve that's going into this tumor?

That's the poetic name for brain tumor, isn't it?

We r emoved a tumor fr om her brain.

Let's say that instead of just knowing the tumor size, we know both the age of the patients and the tumor size.

So, let's say you have a friend who tragically has a tumor. And maybe, their tumor size and age falls around there.

But often there is this intuition that the negative class is conveying the absence of something, like the absence of a malignant tumor, whereas one, the positive class, is conveying the presence of something that we may be looking for.

But the MBI image shows that tumor much more clearly, as well as a second tumor, which profoundly influence that patient's surgical options.

But what about the part that goes into the tumor?

Then the tumor in my belly is a time bomb.

And like a tumor, fat grows when blood vessels grow.

And look at how the tumor sizes plummeted in between.

If you can find a tumor when it's less than a centimeter, survival exceeds 90 percent, but drops off rapidly as tumor size increases.

like this kidney tumor, so that you can exactly see where the boundary is between the kidney tumor and the kidney you want to leave behind, or the liver tumor and the liver you want to leave behind.

This is a little Bangladeshi girl from the east end of London who's got a huge malignant tumor on the right side of her face, which has already made her blind and which is rapidly growing and is going to kill her shortly.

So, in the chicken you get a tumor, in the embryo you don't, you dissociate, you put it in a dish, you get another tumor.

And started to use different symbols, circles and crosses, to denote malignant versus benign examples.

And now, boom, the tumor labels itself and it gets fluorescent.