I want to tune the parameters of the "SVR()" regression function. It starts processing and doesn't stop, I am unable to figure out the problem. I am predicting a parameter using the SVM regression function SVR(). The results are not good with default values in Python.so I want to try tunning it with "GridSearchCv". The last part "grids.fit(Xtrain,ytrain)" start running without giving any error and doesn't stop. SVR() tunning using GridSearch Code:
from sklearn.model_selection import GridSearchCV.
param = {'kernel' : ('linear', 'poly', 'rbf', 'sigmoid'),'C' : [1,5,10],'degree' : [3,8],'coef0' : [0.01,10,0.5],'gamma' : ('auto','scale')},
modelsvr = SVR(),
grids = GridSearchCV(modelsvr,param,cv=5)
grids.fit(Xtrain,ytrain)
It Continues to process without stopping.
Yes, you are right. I have come across the same scenario, when I try to run GridsearchCV for SVR(). The possible reasons are, 1) Your Processor memory(RAM) must be less, 2) The train data sample size is more, equal chance of consuming more time to run Gridsearch since your processor is low memory, so without any error the Job running time will be more.
For your info: I have run Gridsearch with train sample size of 30K using 16GB RAM memory space, it elapsed 210mins to finish the run. So, here patience is must.
Happy Analyzing !!