Selezione del modello: modelli ensemble

Esercitarsi con le domande di colloquio di Machine Learning in Python

Lisa Stuart

Data Scientist

Bootstrapping

¹ https://medium.com/@rrfd/boosting-bagging-and-stacking-ensemble-methods-with-sklearn-and-mlens-a455c0c982de

Foresta

¹ https://www.sca.com/en/about-us/our-forest/

Gradient boosting

¹ https://blog.bigml.com/2017/03/14/introduction-to-boosted-trees/

¹ https://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble

Function	restituisce
`sklearn.ensemble.RandomForestClassifier`	Random Forest
`sklearn.ensemble.GradientBoostingClassifier`	Modello Gradient Boosted
`sklearn.metrics.accuracy_score`	accuratezza del modello addestrato
`sklearn.metrics.confusion_matrix(y_test,y_pred)`	matrice di confusione
`sklearn.metrics.precision_score(y_test,y_pred)`	precisione
`sklearn.metrics.recall_score(y_test,y_pred)`	richiamo
`sklearn.metrics.f1_score(y_test,y_pred)`	f1-score

Esercitarsi con le domande di colloquio di Machine Learning in Python