1
Q
Decision tree
A
A flowchart of decisions when deciding how to classify data points
2
Q
Ensemble learning
A
The use of multiple models on the same data set
3
Q
Bootstrap aggregating (bagging)
A
The random allocation of training data to multiple variants of the same model, then the concatenation of the final results
4
Q
Boosting
A
Subsequent models pay more attention to faulty parts of the model by boosting attributes that caused flaws in the previous models
5
Q
Bucket of models
A
Running multiple different models to determine and use the best one only
6
Q
Stacking
A
Running multiple different models and concatenating their results
Decks in class (4)
# Cards
