Login using Social Account
     Continue with GoogleLogin using your credentials
Let us use logistic regression for this classification problem.
Import GridSearchCV
from sklearn.model_selection
.
from sklearn.model_selection import << your code comes here >>
Import LogisticRegression
from sklearn.linear_model
.
from sklearn.linear_model import << your code comes here >>
Import confusion_matrix, auc, roc_curve
from sklearn.metrics
.
from sklearn.metrics import << your code comes here >>
Let us declare some parameters and their values for the grid-search.
parameters = {"penalty": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
paramter C is the regularization parameter, and penalty is the norm used in the penalization.
Instantiate LogisticRegression()
as lr
.
lr = LogisticRegression()
Pass lr, parameters,cv=5
as arguments to GridSearchCV
.
clf = GridSearchCV(lr, parameters, cv=5, verbose=5, n_jobs=3)
Fit the classifier on X_train_res
and y_train_res
using fit
.
k = clf.<< your code comes here >>(X_train_res, y_train_res)
Let us print the best parameters.
print(k.best_params_)
Taking you to the next exercise in seconds...
Want to create exercises like this yourself? Click here.
Note - Having trouble with the assessment engine? Follow the steps listed here
Loading comments...