Hi

Why do we see difference in output of sgdr class and more iterations with X_Norm as compared to X_train?

Code from Lab:

sgdr = SGDRegressor()

sgdr.fit(X_train, Y_train )

print ( sgdr)

print(f"number of iterations completed: {sgdr.n_iter_}, number of weight updates: {sgdr.t_}")

SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,

eta0=0.01, fit_intercept=True, l1_ratio=0.15,

learning_rate=‘invscaling’, loss=‘squared_loss’, max_iter=1000,

n_iter_no_change=5, penalty=‘l2’, power_t=0.25, random_state=None,

shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,

warm_start=False)

number of iterations completed: 146, number of weight updates: 14455.0

===========

Code modified to pass X_train instead of X_norm to sgdr:

sgdr = SGDRegressor()

sgdr.fit(X_train, Y_train )

print(f"number of iterations completed: {sgdr.n_iter_}, number of weight updates: {sgdr.t_}")

print ( sgdr)

number of iterations completed: 42, number of weight updates: 4159

SGDRegressor()