Fitting a line through data with machine learning

Linear regression with machine learning involves testing the linear regression algorithm on unseen data. Here, we will perform 10-fold cross-validation:

• Split the set into 10 parts
• Train on 9 of the parts and test on the one left over
• Repeat this 10 times so that every part gets to be a test set once

Getting ready

As in the previous section, load the dataset you want to apply linear regression to, in this case, the Boston housing dataset:

from sklearn import datasets
boston = datasets.load_boston()

How to do it...

The steps involved in performing linear regression are as follows:

1. Import the LinearRegression object and create an object:

from sklearn.linear_model import LinearRegression
lr = LinearRegression()

2. Pass the independent and dependent variables to the fit method of LinearRegression:

lr.fit(boston.data, boston.target)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

3. Now, to get the 10-fold cross-validated predictions...