Can gradient descent be used for logistic regression?

Can gradient descent be used for logistic regression?

Surprisingly, the update rule is the same as the one derived by using the sum of the squared errors in linear regression. As a result, we can use the same gradient descent formula for logistic regression as well.

Can we use Gradient Descent for classification?

Logistic Regression is widely used as a classification technique. Let us consider the following regression equation where the response variable y is a categorical variable with two classes. The most basic and vastly used optimisation technique to minimise the above function is Gradient Descent.

What loss function is most suited for Gradient Descent in logistic regression?

Logistic regression models generate probabilities. Log Loss is the loss function for logistic regression. Logistic regression is widely used by many practitioners.

Can we use gradient descent for classification?

How is gradient descent used in logistic regression?

The loss or error on the y axis and number of iterations on the x axis. You might know that the partial derivative of a function at its minimum value is equal to 0. So gradient descent basically uses this concept to estimate the parameters or weights of our model by minimizing the loss function.

Which is the minimum value of gradient descent?

You might know that the partial derivative of a function at its minimum value is equal to 0. So gradient descent basically uses this concept to estimate the parameters or weights of our model by minimizing the loss function. Check out the below video for a more detailed explanation on how gradient descent works.

How is logistic regression similar to linear regression?

Logistic regression is almost similar to Linear regression but the main difference here is the cost function. Logistic Regression uses much more complex function namely log-likelihood Cost function whereas the other uses mean squared error (MSE) as the cost function.

What’s the difference between gradient descent and SGD?

The only difference SGD has with Normal Gradient Descent is that, in SGD, we don’t deal with the entire training instance at a single time. In SGD, we compute the gradient of the cost function for just a single random example at each iteration. Now, doing so brings down the time taken for computations by a huge margin especially for large datasets.