In [1]:

    

from sklearn import datasets, linear_model, metrics
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import math, scipy, numpy as np
from scipy import linalg


    

In [2]:

    

data_set = datasets.load_diabetes()


    

In [3]:

    

x_trn,x_tst,y_trn,y_tst = train_test_split(data_set.data,data_set.target,test_size=0.2)


    

In [5]:

    

x_trn.shape,x_tst.shape,y_trn.shape,y_tst.shape


    

    
Out[5]:





((353, 10), (89, 10), (353,), (89,))

In [7]:

    

feature_names=['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']


    

In [6]:

    

lr = linear_model.LinearRegression()


    

In [ ]:

    

def regr_metrics(act, pred):
    return (math.sqrt(metrics.mean_squared_error(act, pred)), 
     metrics.mean_absolute_error(act, pred))