Mastering Numerical Computing with NumPy

SciPy and scikit-learn

Scikit-learn is one of the SciKit libraries for machine learning, and it's built on top of SciPy. You can use it to perform regression analysis, as you've done in previous chapters with the scikit-learn library. Take a look at this code:

from sklearn import datasets, linear_model from sklearn.metrics import mean_squared_error, r2_score diabetes = datasets.load_diabetes() linreg = linear_model.LinearRegression() linreg.fit(diabetes.data, diabetes.target) # You can inspect the results by looking at evaluation metrics print('Coeff.: n', linreg.coef_) print("MSE: {}".format(mean_squared_error(diabetes.target, linreg.predict(diabetes.data)))) print('Variance Score: {}'.format(r2_score(diabetes.target, linreg.predict(diabetes.data)))) ...