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 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...
