[FIXED] Knowing the number of iterations needed for convergence in SVR scikit-learn

Issue

I am trying to optimize an SVR model and facing a problem because of overfitting, to overcome this I have tried to decrease the number of iterations instead of leaving it until convergence.

To compare the both models I need the number of iterations for both cases. How can I know the number of iterations needed for convergence in the case it is open (max_iter=-1)?

This is my code:

model_1=SVR(kernel='rbf', C=316, epsilon=0, gamma=0.003162,max_iter=2500)
model_1.fit(tr_sets[:,:2],tr_sets[:,2])
print(model_1.score)
model_2=SVR(kernel='rbf', C=316, epsilon=0, gamma=0.003162,max_iter=-1)
model_2.fit(tr_sets[:,:2],tr_sets[:,2])
print(model_2.score)

Edit: the problem now is solved for IPython IDE by setting verbose=2 but still need to be viewed in Jupyter notebook, spyder or to be written to an external file as the verbose option seems only to work with IPython IDE

Solution

If you want to see the progress of your SVR, enter verbose=2 to the constructor of SVR - notice this can make progress slower by a magnitude

from sklearn.svm import SVR
import numpy as np

n_samples, n_features = 10, 5
np.random.seed(0)
y = np.random.randn(n_samples)
X = np.random.randn(n_samples, n_features)
clf = SVR(C=1.0, epsilon=0.2,verbose=2)
clf.fit(X, y)

Output will be

optimization finished, #iter = 4
obj = -4.366801, rho = -0.910470
nSV = 7, nBSV = 5

Where #iter is what you are looking for

Answered By - bluesummers