Why do we use gradient ascent in logistic regression?
The idea behind gradient ascent is that gradients point “uphill”. If you continuously take small steps in the direction of your gradient, you will eventually make it to a local maximum. In the case of logistic regression you can prove that the result will always be a global maximum.
What is descent in gradient descent?
Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent.
Why do we use gradient ascent?
Typically, you’d use gradient ascent to maximize a likelihood function, and gradient descent to minimize a cost function. Both gradient descent and ascent are practically the same.
What is the role of logistic function in logistic regression?
Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression ).
What is loss function in logistic regression?
Loss function for Logistic Regression. The loss function for linear regression is squared loss. The loss function for logistic regression is Log Loss, which is defined as follows: where: \\((x,y)\\in D\\) is the data set containing many labeled examples, which are \\((x,y)\\) pairs.
Why do we use gradient descent in linear regression?
The main reason why gradient descent is used for linear regression is the computational complexity: it’s computationally cheaper (faster) to find the solution using the gradient descent in some cases.
What is gradient descent in linear regression?
Gradient Descent. An algorithm called gradient descent is used for minimizing the cost function J. It turns out gradient descent is a more general algorithm, and is used not only in linear regression. It’s actually used all over the place in machine learning.