Introduction to logistic regression  (Page 2/3)

Sigmoid function

Why might we want to do this? If we are doing classification between two different items, where one set of examples has the value 0 and one has the value 1, it doesn't make much sense to have values that are outside of this range. With linear regression, the predictions can take on any value. In order to model our hypothesis as being contained within the 0 to 1 range, we can use the sigmoid function. Using the Generalized Linear Model,

Recall that in linear regression the hypothesis is

In logistic regression, the hypothesis function is

Wolfram demonstrations logit function

Probabilistic interpretation

What we are essentially doing with taking least-squares regression is fitting our data, but we can go about classifying by describing the probability boundary that one of our points is above and below a line, and finding the maximum likelihood estimate of a given theta.

If we define the Probabilities of being defined as class 1 or 0 as

Then it becomes clear that the likelihoods are described as the following:

From statistics, it is well-known that taking the log of a maximum likelihood estimate will still achieve the same maximum, and calculating the log-likelihood is significantly easier from a computational standpoint. For a proof of this, see here .

We therefore take the log of the above cost function as a log-likelihood and obtain:

Minimizing the cost function

Now that we understand how we would classify these datasets exactly, how do we minimize the cost function? One simple way involves the application of Gradient Descent.

Gradient Descent is a method of approximating a cost function that gets you to converge to a final correct value. This is described for our cost function above as

That is, for every example, we update the theta value by subtracting (subject to some learning rate parameter $\alpha$ ) the partial derivative of the cost function in terms of that example, and repeat until convergence.

If we plot the output of a gradient descent function, it will start at a random point on the contour plot, and then after every iteration, it will move closer to the optimal value.

Gradient descent

Applying logistic regression

In this section, we apply logistic regression described in earlier section to a simulated data set and study how well it performs.

Data generation

We simulated the case where each training example is described by two features $x_1$ and $x_2$ . The two features $x_1$ and $x_2$ are generated uniformly in the range $[0,10]$ . The size of the training data set is 1000. The training examples were split into 2 classes, class 0 or class 1 based on the polynomial:

All the training examples that are above the polynomial eq.  [link] belong to class 1 and the training examples below the polynomial curve belong to class 0. Notice that the true boundary that separates the two classes is a 4 ^th order polynomial.