# Choose the type of classifier. xgb tuned $=$ XGBClassifier$($random$\_$state$=1,$ eval$\_$metric$=$"logloss"$)$ # Grid of parameters to choose from parameters $=\{"$n$\_$estimators": np$.$arange$(50,110,25),$ "scale$\_$pos$\_$weight": $[1,2,5],$ "subsample": $[0\mathrm{.}9,1],$ "learning$\_$rate": $[0\mathrm{.}01,0\mathrm{.}1,0\mathrm{.}05],$ "gamma": $[1,3]\}$ # Type of scoring used to compare parameter combinations acc$\_$scorer $=$ metrics.make$\_$scorer$($metrics$.$f$1\_$score$)$ # Run the grid search grid$\_$obj $=$ GridSearchCV$($xgb$\_$tuned, parameters, scoring$=$acc$\_$scorer, cv$=5)$ grid$\_$obj $=$ grid$\_$obj.fit$($X$\_$train, y$\_$train$)$ # Set the clf to the best combination of parameters xgb$\_$tuned $=$ grid$\_$obj.best$\_$estimator$\_$ # Fit the best algorithm to the data. xgb$\_$tuned.fit$($X$\_$train, y$\_$train$)$ NameError Traceback $($most recent call last$)$ Cell In$[85],$ line $21$ # Choose the type of classifier. $---->2$ xgb$\_$tuned $=$ XGBClassifier$($random$\_$state$=1,$ eval$\_$metric$=$"logloss"$)4$ # Grid of parameters to choose from $5$ parameters $=\{6"$n$\_$estimators": np$.$arange$(50,110,25),7$ "scale$\_$pos$\_$weight": $[1,2,5],(...)10$ "gamma": $[1,3]11\}$ NameError: name 'XGBClassifier' is not defined