models $=[]$ # Empty list to store all the models

# Appending models into the list

models.append$(("$Bagging$",$ BaggingClassifier$($random$\_$state$=1)))$

models.append$(("$Random forest", RandomForestClassifier$($random$\_$state$=1)))$

$\text{'}\_\_\_\_\_\_\_\text{'}$ ## Complete the code to append remaining $3$ models in the list models

print$("$

$"$ "Training Performance:" $"$

$")$

for name, model in models:

model.fit$($X$\_$train$\_$over, y$\_$train$\_$over$)$

scores $=$ recall$\_$score$(\_\_\_\_\_,$ model.predict$(\_\_\_\_\_))$ ## Complete the code to build models on oversampled data

print$("\{\}$: $\{\}".$format$($name$,$ scores$))$

print$("$

$"$ "Validation Performance:" $"$

$")$

for name, model in models:

model.fit$($X$\_$train$\_$over, y$\_$train$\_$over$)$

scores $=$ recall$\_$score$($y$\_$val, model.predict$($X$\_$val$))$

print$("\{\}$: $\{\}".$format$($name$,$ scores$))$