Hello,
I'm having trouble running logistic regression with randomized search cv.
`param_test = {'penalty': ['l1', 'l2'],
'C': [1 , 0.5, 0.1, 0.05, 0.01],
'class_weight': ['balanced', None],
'solver': ['liblinear', 'lbfgs'],
'max_iter': [100,200,300]}
n_HP_points_to_test=10
clf = LogisticRegression()
gs = RandomizedSearchCV(
estimator=clf, param_distributions=param_test,
n_iter=n_HP_points_to_test,
scoring='accuracy',
cv=3,
refit=True,
random_state=314,
verbose=True)
gs.fit(X_train_scaled, y_train)`
and this is the error message I get: TypeError: Cannot clone object '<interpret.glassbox.linear.LogisticRegression object at 0x7f7f18125250>' (type <class 'interpret.glassbox.linear.LogisticRegression'>): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' method.
Thanks,
Noga
Hello,
I'm having trouble running logistic regression with randomized search cv.
`param_test = {'penalty': ['l1', 'l2'],
'C': [1 , 0.5, 0.1, 0.05, 0.01],
'class_weight': ['balanced', None],
'solver': ['liblinear', 'lbfgs'],
'max_iter': [100,200,300]}
n_HP_points_to_test=10
clf = LogisticRegression()
gs = RandomizedSearchCV(
estimator=clf, param_distributions=param_test,
n_iter=n_HP_points_to_test,
scoring='accuracy',
cv=3,
refit=True,
random_state=314,
verbose=True)
gs.fit(X_train_scaled, y_train)`
and this is the error message I get: TypeError: Cannot clone object '<interpret.glassbox.linear.LogisticRegression object at 0x7f7f18125250>' (type <class 'interpret.glassbox.linear.LogisticRegression'>): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' method.
Thanks,
Noga