Mulder's test evaluates for Morton neuroma.

DOCTOR The growth is malignant.

We can revert the malignant phenotype.

And from the evil of malignant witchcraft,

If an audible click is heard, it's called a Mulder's sign and indicative of a fibrotic neuroma.

And in order to show you that the malignant phenotype

I'm afraid the tests are positive. The mass is malignant.

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

We're waiting for test results in order to see if it's malignant

that I have been diagnosed with a highly malignant form of cancer.

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

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

such as malignant cancers, leukemia and radiation cataracts that have continued to affect generations of family members.

Learning, we were told explicitly what is the so called right answer, whether it's benign or malignant.

I wish I'd had conventional reconstructive techniques rather than this because I'm now dying of a malignant cancer ?

I'm going to use different symbols to denote my benign and malignant, or my negative and positive examples.

I didn't just choose one, here are little movies, sort of fuzzy, but you see that on the left are the malignant cells, all of them are malignant, we add one single inhibitor in the beginning, and look what happens, they all look like that.

Let's see you want to look at medical records and try to predict of a breast cancer as malignant or benign.

And sadly we also saw a few malignant tumors, one of that size, one of that size, one of that size...

And I'm going to keep using X's to denote my malignant tumors. Okay? I hope this is beginning to make sense.

That's another classification problem, and earlier we also talked about the example of classifying tumors as a cancerous malignant or as benign tumors.

I mean, it should be no surprise to the learning out of that the example way out here turns out to be malignant.

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

The term classification refers to the fact that here we're trying to predict a discrete value output zero or one, malignant or benign.

And a different set of patients, they look a little different, whose tumors turn out to be malignant, as denoted by the crosses.

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.

I have five examples of benign tumors shown down here, and five examples of malignant tumors shown with a vertical axis value of one.

The wrongs which we seek to condemn and punish, have been so calculated, so malignant and so devastating, that civilization cannot tolerate their being ignored.

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.

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.

Hence, you see, my double deduction that you had been out in vile weather, and that you had a particularly malignant boot slitting specimen of the London slavey.

Hence, you see, my double deduction that you had been out in vile weather, and that you had a particularly malignant boot slitting specimen of the London slavey.

Y that we can think of as taking on two values, either zero or one, either a spam or not spam, fraudulent or not fraudulent, malignant or benign.

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.

This is a man in his 20s whose first visit out of Nigeria was with this malignant cancer that he came to the United Kingdom to have operated on.

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.

So, they put the cells in cultures, chicken cells, dumped the virus on it, and it would pile up, and they would say, this is malignant and this is normal.

So this is a photograph of Henry, two weeks after he had a malignant cancer removed from the left side of his face his cheekbone, his upper jaw, his eye socket.

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.

Well, everything to the right of somewhere around here to the right of this we should predict as positive, and everything to the left we should probably predict as negative because from this training set it looks like all the tumors larger than, you know, a certain value around here are malignant, and all the tumors smaller than that are not malignant, at least for this training set.

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.

like a heart, or liver, or lung transplant it is a quality of life transplant, and as a result, are the patients going to say, if they get a malignant cancer 10 or 15 years on,

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.

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.