Contents
Why is the mean squared error not suitable as a cost function in logistic regression?
The main reason not to use the MSE as the cost function for logistic regression is because you don’t want your cost function to be non-convex in nature. If the cost function is not convex then its difficult for the function to optimally converge.
What is considered a good RMSE?
Based on a rule of thumb, it can be said that RMSE values between 0.2 and 0.5 shows that the model can relatively predict the data accurately. In addition, Adjusted R-squared more than 0.75 is a very good value for showing the accuracy. In some cases, Adjusted R-squared of 0.4 or more is acceptable as well.
Is the probability of gradient descent always the same?
Gradient Descent is an algorithm which is designed to find the optimal points, but these optimal points are not necessarily global. And yes if it happens that it diverges from a local location it may converge to another optimal point but its probability is not too much.
Is it possible for gradient descent to converge?
And yes if it happens that it diverges from a local location it may converge to another optimal point but its probability is not too much. The reason is that the step size might be too large that prompts it recede one optimal point and the probability that it oscillates is much more than convergence.
How does gradient descent work in machine learning?
Gradient Descent is a machine learning algorithm that operates iteratively to find the optimal values for its parameters. It takes into account, user-defined learning rate, and initial parameter values. How does it work? Start with initial values.
When does gradient descent stop running in real life?
In real-life scenarios, we do not do that. The actual stop point for gradient descent to stop running should be when step size approaches zero. Let us check how the L2 Loss reduces along with increasing iterations by plotting a graph. In the above graph, we see that initially, the error reduces significantly.