How to plot number of Trees and OOBs score with Grid Search I searched to find the answer but I don´t find something with Grid Search.
I create a random forest and gradient boosting regressor with grid search. Now I want to make a visualization to see if the number of trees is alright.
# Grid-Search erstellen auf der Basis eineer Random Search 
param_grid = {
    'bootstrap': [True],
    'max_depth': [50, None],
    'max_features': ['auto', 50],
    'min_samples_leaf': [5],
    'min_samples_split': [2],
    'n_estimators': [100, 150, 200, 250, 300, 350]
}

# Base-Model zum Verbessern erstellen
rf_v1 = RandomForestRegressor(random_state=42)

# Grid-Search initisieren 
rf_grid_search_v1 = GridSearchCV(estimator = rf_v1, param_grid = param_grid, 
                           scoring = 'neg_mean_absolute_error', cv = 3, 
                           n_jobs = -1, verbose = 2)

# Die Grid-Search für das Model durchführen
rf_grid_search_v1.fit(X_train_v1, y_train_v1)

But how to plot the splits of trees with gridsearch? Is there any way to make an RF/xgb trees split visualization?
I am really grateful if anybody has a tip for me.
To plot feature importance using gridsearch use:

x= X_train_v1.columns,y= rf_grid_search_v1.best_estimator_.feature_importances_