Analysing TPOT's stability
You will now see the random nature of TPOT by constructing the classifier with different random states and seeing what model is found to be best by the algorithm. This assists to see that TPOT is quite unstable when not run for a reasonable amount of time.
Este ejercicio forma parte del curso
Hyperparameter Tuning in Python
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# Create the tpot classifier
tpot_clf = TPOTClassifier(generations=2, population_size=4, offspring_size=3, scoring='accuracy', cv=2,
verbosity=2, random_state=____)
# Fit the classifier to the training data
tpot_clf.fit(X_train, y_train)
# Score on the test set
print(tpot_clf.score(X_test, y_test))