Linear and logistic regressions are the two methods that can be used to linearly predict a target value and target class respectively. Let's start with an example of linear regression.
In this section, we will use the Boston dataset, which contains 506 samples, 13 features (all real numbers), and a (real) numerical target. We will divide our dataset into two sections by using a so-called train/test split cross-validation to test our methodology (in the example, 80 percent of our dataset goes in training, and 20 percent in the test):
In: from sklearn.datasets import load_boston boston = load_boston() from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=0)
The dataset is now loaded and the train/test pairs have been created. In the next few steps, we're going to train and fit the regressor in the training set and predict the target variable in the test...