Linear and logistic regression

Linear and logistic regressions are the two methods that can be used to linearly predict a target value or a target class, respectively. Let's start with an example of linear regression predicting a target value.

In this section, we will again use the Boston dataset, which contains 506 samples, 13 features (all real numbers), and a (real) numerical target (which renders it ideal for regression problems). We will divide our dataset into two sections by using a train/test split cross-validation to test our methodology (in the example, 80 percent of our dataset goes in training and 20 percent in test):

In: from sklearn.datasets import load_boston
boston = load_boston()
from sklearn.cross_validation import train_test_split ...

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