Sklearn also provides a lot of metrics you can use for evaluation such as MSE. That means we can use gradient descent to find the optimal coefficients for our model!One reason that gradient descent is more common than the normal equation for machine learning is that it scales much better as we increase the number of features. It is a very simple, yet powerful algorithm that can be used to find the minimum of a function.This technique will find the global minimum if a function is convex, if not, we can only prove that it will find a local minimum. Some user-contributed text on this page is available under the 5



A ton of business applications just needs a quality regression model for point prediction.Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. And both are pretty close, which suggests we don’t have an overfitting problem. Last edit on Feb 13, 2014.




In that case, this wouldn’t be a high bias problem. So — we want to keep our testing set untainted in the sense that in a perfect world we would only run our tests on it after we have done all of our experimentation and think we have found the very best model.It sounds like we need a third set of data — a validation set.

Then, since we don’t care about the direction of the error, we square the difference.

In many circumstances it makes sense to give more weight to points further away from the mean — that is, being off by 10 is more than twice as bad as being off by 5. You can think of regularization as a method used to penalize a model from learning complex relationships.



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So far, we have learned that it is a good idea to split your data into training and testing sets to better understand how well the model is actually doing. A learning curve plots our error function (MSE) with various amounts of data used for training. In sklearn, it is called alpha.

It is also a general optimization technique that pops up all over machine learning, so understanding how it works is extremely valuable.Gradients are just the partial derivatives with respect to the coefficients.

Now we have RMSE on both our training and testing data. Up to this point, we have been training on all the data that we have.

Typically, many values can be tried and here are a few that I believe are suggested by Andrew Ng: .001, .003, .01, .03, .1, .3, 1, 3Choosing a value that is too small, leads to slower convergence.


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