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Let us predict the labels for train and test data, get the confusion matrix, and calculate the recall values.

**Note:**

`confusion_matrix`

: computes confusion matrix to evaluate the accuracy of classification.By definition, a confusion matrix

*C*is such that*Ci,j*is equal to the number of observations known to be in the group*i*and predicted to be in group*j*.Thus in binary classification, the count of true negatives is

*C00*, false negatives is*C10*, true positives is*C11*and false positives is*C01*.

`recall`

is calculated by (true positives)/(true positives + false negatives). Note that we are calculating recall value because we want to detect fraudulent credit card transactions. It might be tolerable to classify some valid transactions as fraudulent, but it is not tolerable to misclassify the fraudulent transactions as valid ones.

Store the best estimator from the gridsearchcv in

`lr_gridcv_best`

.`lr_gridcv_best = clf.best_estimator_`

Use

`predict`

method of`lr_gridcv_best`

on`X_test`

and store the predictions in`y_test_pre`

.`y_test_pre = lr_gridcv_best.<< your code comes here >>(X_test)`

Call the

`confusion_matrix`

function imported from`sklearn.metrics`

. Pass`y_test, y_test_pre`

as arguments.`cnf_matrix_test = << your code comes here >>(y_test, y_test_pre)`

Calculate the recall for test data predictions by the best model.

`print("Recall metric in the test dataset:", (cnf_matrix_test[1,1]/(cnf_matrix_test[1,0]+cnf_matrix_test[1,1] )))`

Use

`predict`

method of`lr_gridcv_best`

on`X_train_res`

and store the predictions in`y_train_pre`

.`y_train_pre = lr_gridcv_best.<< your code comes here >>(X_train_res)`

Call the

`confusion_matrix`

function imported from`sklearn.metrics`

. Pass`y_train_res, y_train_pre`

as arguments.`cnf_matrix_train = << your code comes here >>(y_train_res, y_train_pre)`

Calculate the recall for resampled train data predictions by the best model.

`print("Recall metric in the train dataset:", (cnf_matrix_train[1,1]/(cnf_matrix_train[1,0]+cnf_matrix_train[1,1] )))`

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