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

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.

Rebecca has a benign blood vessel tumor that's growing out through her skull, has obliterated her nose, and she's having difficulty seeing.

So let's say our data set looks like this where we saw a tumor of this size that turned out to be benign.

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

There's nothing benign about it.

Benign advice now, profoundly mistaken.

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.

Please help is a benign violation.

So, for example, weather is benign.

So here's a tumor.

You know, with this, hopefully you can decide that your friend's tumor is more likely to if it's over there, that hopefully your learning algorithm will say that your friend's tumor falls on this benign side and is therefore more

Finally, we distinguish benign versus adversarial environments.

Fortunately, her lump proved to be benign.

Tom had a brain tumor.

You don't have a tumor.

or a recently diagnosed tumor.

What is it? A tumor?

like a fairly benign cartoon within this context.

In benign environments, the environment might be random.

That's counterindicative for a hemorrhagic 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.

It all sort of started as a benign mistake,

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.

And your father called you the tumor.

He has extensive experience in tumor removal.

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.

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.

Walt, your tumor has shrunk by 80 percent.

Not unless a tumor is made of glass.

There's a concept about humor about it being a benign violation.

And at first that sounds very nice, very benign, well intended.

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.

Night terrors can be caused by a brain tumor.