Hello everyone, my name is Abhajha and today I am going to show you what is the role of the alpha parameter that is the learning rate in case of machine learning. So first of all, what is the learning rate alpha? So basically it is a parameter which is used to control how fast or how slow the model learns in case of the gradient descent approach. So what happens is if the learning rate is lower than the optimal value, then what happens that the number of iterations or the number of epochs used to minimize the cost function basically increases and it basically takes more time to do the same thing. And if the learning rate is higher than usual, then what happens is that the error generally saturates at much higher value than expected. So here on the right hand side you can see this image where we can see that there is a local minima and there is a global minima. So what basically happens is that if the learning rate is too low, it basically the model basically gets stuck at the local minima for most of the time and it takes longer time to reach the global minima and same is true with the higher learning rate. So if the learning rate is too high, then basically the model does not stop at the global minima and that is why the constant error basically increases. So now we will look at how to test the robustness of the machine learning model. So for that we have a parameter which is called the R squared value. So basically this is a parameter which can be used to predict the robustness of the model and basically what happens is the higher the R squared value, the better is the machine learning model. So this is one of the ways to show how robust the model is because the R squared basically corresponds to the correlation between the observed outcomes and predicted outcomes. So and apart from this we can also use the F test which is a statistical test to check whether our hypothesis is robust or not. So basically this is how you determine the robustness of your machine learning model. Thank you.