GridSearchCV Lasso warnings ConvergenceWarning: Objective did not converge
0
I am trying to find the best params for my data using CVGridSearch
lasso = Lasso(random_state=0)
alphas = [0.5, 0.1 , 0.01 ]
max_iter = [1000, 2000, 3000]
tuned_parameters = [{'alpha': alphas , 'max_iter' : max_iter}]
n_folds = 5
clf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit=False)
clf.fit(X_train, y_train.values.ravel())
When I run the code, it shows me following warnings
Best: 0.999998 using {'alpha': 0.1, 'max_iter': 1000}
Best: -2028.743734 using {'alpha': 0.1, 'max_iter': 1000}
/usr/local/lib/python3.6/site-
packages/sklearn/linear_model/coordinate_descent.py:492:
ConvergenceWarning: Objective did not converge. You might want to
increase the number of iterations. Fitting data with very small alpha
may cause precision problems.
ConvergenceWarning)
Best: -2241.410408 using {'alpha': 0.01, 'max_iter': 1000}
/usr/local/lib/python3.6/site-
packages/sklearn/linear_model/coordinate_descent.py:492:
ConvergenceWarning: Objective did not converge. You might want to
increase the number of iterations. Fitting data with very small alpha
may cause precision problems.
When the best score 0.999998 is found, why the CVGridsearch doesn't stop?
python scikit-learn
edited Nov 28 '18 at 7:09
Vivek Kumar
16.4k42155
asked Nov 27 '18 at 15:31
A.SA.S
1
add a comment |
0
I am trying to find the best params for my data using CVGridSearch
lasso = Lasso(random_state=0)
alphas = [0.5, 0.1 , 0.01 ]
max_iter = [1000, 2000, 3000]
tuned_parameters = [{'alpha': alphas , 'max_iter' : max_iter}]
n_folds = 5
clf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit=False)
clf.fit(X_train, y_train.values.ravel())
When I run the code, it shows me following warnings
Best: 0.999998 using {'alpha': 0.1, 'max_iter': 1000}
Best: -2028.743734 using {'alpha': 0.1, 'max_iter': 1000}
/usr/local/lib/python3.6/site-
packages/sklearn/linear_model/coordinate_descent.py:492:
ConvergenceWarning: Objective did not converge. You might want to
increase the number of iterations. Fitting data with very small alpha
may cause precision problems.
ConvergenceWarning)
Best: -2241.410408 using {'alpha': 0.01, 'max_iter': 1000}
/usr/local/lib/python3.6/site-
packages/sklearn/linear_model/coordinate_descent.py:492:
ConvergenceWarning: Objective did not converge. You might want to
increase the number of iterations. Fitting data with very small alpha
may cause precision problems.
When the best score 0.999998 is found, why the CVGridsearch doesn't stop?
python scikit-learn
edited Nov 28 '18 at 7:09
Vivek Kumar
16.4k42155
asked Nov 27 '18 at 15:31
A.SA.S
1
Show the full code. The output you are showing is not produced by the above code.
– Vivek Kumar
Nov 28 '18 at 9:48
add a comment |
0
0
0
I am trying to find the best params for my data using CVGridSearch
lasso = Lasso(random_state=0)
alphas = [0.5, 0.1 , 0.01 ]
max_iter = [1000, 2000, 3000]
tuned_parameters = [{'alpha': alphas , 'max_iter' : max_iter}]
n_folds = 5
clf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit=False)
clf.fit(X_train, y_train.values.ravel())
When I run the code, it shows me following warnings
Best: 0.999998 using {'alpha': 0.1, 'max_iter': 1000}
Best: -2028.743734 using {'alpha': 0.1, 'max_iter': 1000}
/usr/local/lib/python3.6/site-
packages/sklearn/linear_model/coordinate_descent.py:492:
ConvergenceWarning: Objective did not converge. You might want to
increase the number of iterations. Fitting data with very small alpha
may cause precision problems.
ConvergenceWarning)
Best: -2241.410408 using {'alpha': 0.01, 'max_iter': 1000}
/usr/local/lib/python3.6/site-
packages/sklearn/linear_model/coordinate_descent.py:492:
ConvergenceWarning: Objective did not converge. You might want to
increase the number of iterations. Fitting data with very small alpha
may cause precision problems.
When the best score 0.999998 is found, why the CVGridsearch doesn't stop?
python scikit-learn
edited Nov 28 '18 at 7:09
Vivek Kumar
16.4k42155
asked Nov 27 '18 at 15:31
A.SA.S
1
I am trying to find the best params for my data using CVGridSearch
lasso = Lasso(random_state=0)
alphas = [0.5, 0.1 , 0.01 ]
max_iter = [1000, 2000, 3000]
tuned_parameters = [{'alpha': alphas , 'max_iter' : max_iter}]
n_folds = 5
clf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit=False)
clf.fit(X_train, y_train.values.ravel())
When I run the code, it shows me following warnings
Best: 0.999998 using {'alpha': 0.1, 'max_iter': 1000}
Best: -2028.743734 using {'alpha': 0.1, 'max_iter': 1000}
/usr/local/lib/python3.6/site-
packages/sklearn/linear_model/coordinate_descent.py:492:
ConvergenceWarning: Objective did not converge. You might want to
increase the number of iterations. Fitting data with very small alpha
may cause precision problems.
ConvergenceWarning)
Best: -2241.410408 using {'alpha': 0.01, 'max_iter': 1000}
/usr/local/lib/python3.6/site-
packages/sklearn/linear_model/coordinate_descent.py:492:
ConvergenceWarning: Objective did not converge. You might want to
increase the number of iterations. Fitting data with very small alpha
may cause precision problems.
When the best score 0.999998 is found, why the CVGridsearch doesn't stop?
python scikit-learn
python scikit-learn
edited Nov 28 '18 at 7:09
Vivek Kumar
16.4k42155
asked Nov 27 '18 at 15:31
A.SA.S
1
edited Nov 28 '18 at 7:09
Vivek Kumar
16.4k42155
asked Nov 27 '18 at 15:31
A.SA.S
1
edited Nov 28 '18 at 7:09
Vivek Kumar
16.4k42155
edited Nov 28 '18 at 7:09
Vivek Kumar
16.4k42155
edited Nov 28 '18 at 7:09
Vivek Kumar
16.4k42155
16.4k42155
asked Nov 27 '18 at 15:31
A.SA.S
1
asked Nov 27 '18 at 15:31
A.SA.S
1
asked Nov 27 '18 at 15:31
A.SA.S
1
1
Show the full code. The output you are showing is not produced by the above code.
– Vivek Kumar
Nov 28 '18 at 9:48
add a comment |
Show the full code. The output you are showing is not produced by the above code.
– Vivek Kumar
Nov 28 '18 at 9:48
Show the full code. The output you are showing is not produced by the above code.
– Vivek Kumar
Nov 28 '18 at 9:48
Show the full code. The output you are showing is not produced by the above code.
– Vivek Kumar
Nov 28 '18 at 9:48
add a comment |
0
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Show the full code. The output you are showing is not produced by the above code.
– Vivek Kumar
Nov 28 '18 at 9:48