 # Training the Classification Algorithm

Let us use logistic regression for this classification problem.

INSTRUCTIONS
• 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_)
``````

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