# Maximum Likelihood and Cross Entropy

## Maximum Likelihood and Cross Entropy

## Maximum Likelihood

Let’s say that we have 2 models: one that tells me that my probability of getting accepted is `80%`

and a second one that tells me that the probability is `55%`

. **Which model is more accurate?** If I get accepted, then I would say that the first model is more accurate, whereas, if I get rejected, I would say that the second model is more accurate. Now, that’s just for me. Suppose you also include your friend, in this case, the best model will be the one that gives *higher probability* to the events that happened to *both* of us - whether it is accepted or rejected. This method is called **maximum likelihood**. What we do is we pick the model that gives the existing labels the highest probability. Thus, by maximizing the probability, we can pick the best model.

By maximizing the probability, we pick the best model.

### Understanding what maximum likelihood is through an example

Let’s consider 4 points, two blue and two red. Which model looks better? Obviously the model on the right looks much better as it classifies all the points correctly.

Now let’s see why the model on the right is better from a *probability perspective*. Let’s recall, that our prediction y-hat, is the probability of a point being labeled positive/blue. `y-hat = P(blue)`

. So for the points in the figure, let’s say that our model tells us that the probability of being blue is as shown:

Notice that the points in the blue region are much more likely to be blue, and the points in the red region are much less likely to be blue. Similarly, the probability of the points being red are shown below:

Now, our goal is to calculate the probability of the four points are of the colors that they actually are. In other words, the probability that the two red points are red, and the two blue points are blue. Now if we assume them to be independent events then we have the overall probability as the product of these individual probabilites.

If we do the same thing to the model on the right, we get a higher probability. Thus, we conclude that the model on the right is better.

So, our new *goal* is to maximize the probability, and this method is known as Maximum Likelihood.

### Relation b/w probability and error function

A better model gives us a better probability. Now, the question is **How do we maximize this probability?** or in other words, how do we minimize the error? Can we define an Error function using probability?

Maximizing the probability is equivalent to minimizing the error.

### Problem with products

So we have our two models and we calculated the probabilities. But, what happens when there are hunderds of data points? We will have to take the product for all of them, and it will be an infinitesimally small number.

To fix this, we convert the products into sums using **log**. So, simply by taking the logarithm of the product of the probabilities, we get our Error Function. However, since the probabilities are b/w 0 and 1, we always get negative numbers.

Thus, our Error function is the `-log(Probabilities)`

, which is called **Cross Entropy**.

A good model has

low cross entropy. Let’s see why.

## Cross Entropy

If we calculate the probabilities and pair the points with the corresponding logarithms, we actually get an error for each point. If you look carefully at the values, we can see that the points that are mis-classified have **large** values and the points that are correctly classified have **small** values.

And the reason for this is again that a correctly classified point will have a probability that is close to 1, which when we take the negative of the logarithm, we’ll get a small value. Thus, we can think of the negatives of these logarithms as errors at each point.

Points that are correctly classified will have small errors and points that are mis-classified will have large errors. In this manner, the

Cross Entropy will tell us if a model is good or bad.

So, in other words, our **goal** has changed from maximizing the probability to minimizing the cross entropy, in order for us to get from the model in the left to the model in the right.

Cross Entropy is an Error Function.

Cross Entropy is kind of a big deal. Cross Entropy really says the following:

If I have a bunch of events(points) and a bunch of probabilities, how likely is it that those events happen based on the probabilities? If it is very

likely, then cross entropy is small and if it isunlikelythen the cross entropy is large.