Question

Describe how and why you should partition your data when using classification techniques like k-nearest neighbors and logistic regression.

Answer 1

Partitioning is now a days generally referred as Test Train set splitting process of the data. For logistic / knn we can split the data as follows.

A brief description of the process is given below

• training set—a subset to train a model. (Generally 70-80% of the data)
• test set—a subset to test the trained model. (Generally 30-20% of the data)

You could imagine slicing the single data set as follows:

Slicing a single data set into a training set and test set.

Make sure that your test set meets the following two conditions:

• Is large enough to yield statistically meaningful results.
• Is representative of the data set as a whole. In other words, don't pick a test set with different characteristics than the training set.

Assuming that your test set meets the preceding two conditions, your goal is to create a model that generalizes well to new data. Our test set serves as a proxy for new data. For example, consider the following figure. Notice that the model learned for the training data is very simple. This model doesn't do a perfect job—a few predictions are wrong. However, this model does about as well on the test data as it does on the training data. In other words, this simple model does not over-fit the training data.

Hope the above answer has helped you in understanding the problem. Please upvote the ans if it has really helped you. Good Luck!!