difference between a cost function and the gradient descent equation in logistic regression

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I'm not able to understand the difference between the cost function and the gradient. There are examples on the net where people compute the cost function and then there are places where they don't and just go with the gradient descent function

What is the difference between the two if any?
Feb 22 in Machine Learning by Dev
• 6,000 points
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1 answer to this question.

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Cost function is a way to evaluate the performance of the model/algorithm. So if the predicted values differ a lot  from the actual values then this cost function will be high. This also indicates that the algorithm is not performing well, or not learning well from the data.
While Gradient descent is used for finding a local minimum, it is an optimization algorithm used to train machine learning models and neural networks.
Gradient descent helps to find the best parameters that minimize the model’s cost function.
answered Feb 22 by Nandini
• 5,480 points

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