Model results using GridSearchCV
You discovered that the best parameters for your model are that the split criterion should be set to 'gini', the number of estimators (trees) should be 30, the maximum depth of the model should be 8 and the maximum features should be set to "log2".
Let's give this a try and see how well our model performs. You can use the get_model_results() function again to save time.
To ćwiczenie jest częścią kursu
Fraud Detection in Python
Instrukcje do ćwiczenia
- Input the optimal settings into the model definition.
- Fit the model, obtain predictions and get the performance parameters with
get_model_results().
Interaktywne ćwiczenie praktyczne
Spróbuj tego ćwiczenia, uzupełniając ten przykładowy kod.
# Input the optimal parameters in the model
model = RandomForestClassifier(class_weight={0:1,1:12}, ____='____',
____=____, ____='log2', min_samples_leaf=10, ____=____, n_jobs=-1, random_state=5)
# Get results from your model
get_model_results(____, ____, ____, ____, ____)