Decision Tree

Question 1

Decision tree

Answer 1

A flowchart of decisions when deciding how to classify data points

Question 2

Ensemble learning

Answer 2

The use of multiple models on the same data set

Question 3

Bootstrap aggregating (bagging)

Answer 3

The random allocation of training data to multiple variants of the same model, then the concatenation of the final results

Question 4

Boosting

Answer 4

Subsequent models pay more attention to faulty parts of the model by boosting attributes that caused flaws in the previous models

Question 5

Bucket of models

Answer 5

Running multiple different models to determine and use the best one only

Question 6

Stacking

Answer 6

Running multiple different models and concatenating their results