38. Speeding Up Via Feature Selection 1

Question:

You found in the SVM mini-project that the parameter tune can significantly speed up the training time of a machine learning algorithm. A general rule is that the parameters can tune the complexity of the algorithm, with more complex algorithms generally running more slowly.

Another way to control the complexity of an algorithm is via the number of features that you use in training/testing. The more features the algorithm has available, the more potential there is for a complex fit. We will explore this in detail in the “Feature Selection” lesson, but you’ll get a sneak preview now.

What's the number of features in your data? (Hint: the data is organized into a numpy array where the number of rows is the number of data points and the number of columns is the number of features; so to extract this number, use a line of code like len(features_train[0]).)