python - Why my SGD is far off than my linear regression model? -

i'm trying compare linear regression (normal equation) sgd looks sgd far off. doing wrong?

here's code

x = np.random.randint(100, size=1000) y = x * 0.10 slope, intercept, r_value, p_value, std_err = stats.linregress(x=x, y=y) print("slope %f , intercept %s" % (slope,intercept)) #slope 0.100000 , intercept 1.61435309565e-11 

and here's sgd

x = x.reshape(1000,1) clf = linear_model.sgdregressor() clf.fit(x, y, coef_init=0, intercept_init=0)  print(clf.intercept_) print(clf.coef_)  #[  1.46746270e+10] #[  3.14999003e+10] 

i have thought coef , intercept same data linear.

when tried run code, got overflow error. suspect you're having same problem, reason, it's not throwing error.

if scale down features, works expected. using scipy.stats.linregress:

>>> x = np.random.random(1000) * 10 >>> y = x * 0.10 >>> slope, intercept, r_value, p_value, std_err = stats.linregress(x=x, y=y) >>> print("slope %f , intercept %s" % (slope,intercept)) slope 0.100000 , intercept -2.22044604925e-15 

using linear_model.sgdregressor:

>>> clf.fit(x[:,none], y) sgdregressor(alpha=0.0001, epsilon=0.1, eta0=0.01, fit_intercept=true,        l1_ratio=0.15, learning_rate='invscaling', loss='squared_loss',        n_iter=5, penalty='l2', power_t=0.25, random_state=none,        shuffle=false, verbose=0, warm_start=false) >>> print("slope %f , intercept %s" % (clf.coef_, clf.intercept_[0])) slope 0.099763 , intercept 0.00163353754797 

the value slope little lower, i'd guess that's because of regularization.