Support vector regression

We will capitalize on the SVM classification recipes by performing support vector regression on scikit-learn's diabetes dataset.

Getting ready

Load the diabetes dataset:

#load the libraries we have been using
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn import datasets

diabetes = datasets.load_diabetes()

X = diabetes.data
y = diabetes.target

Split the data in training and testing sets. There is no stratification for regression in this case:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)

How to do it...

1. Create a OneVsRestClassifier within a pipeline and support vector regression (SVR) from sklearn.svm:

from sklearn.svm import SVR
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.multiclass import OneVsRestClassifier

svm_est = Pipeline([('scaler',StandardScaler()),('svc',OneVsRestClassifier...