Fill-in-the-Blank Questions on SVM and Trees

A Support Vector Machine is mainly used for ________ and regression tasks.

In SVM, the main focus is to create the best fitting ________ that separates different classes.

Support vectors are the data points that are ________ to the hyperplane in SVM.

The distance between the support vectors and the hyperplane is referred to as the ________ in SVM.

Kernel functions are used in SVM to transform data into a ________ dimensional feature space.

In Decision Trees, the starting point is known as the ________ node.

The terminal nodes of a decision tree that represent final outcomes are called ________ nodes.

A decision tree splits data based on questions at each internal ________.

Random Forest is a technique that uses multiple ________ to improve prediction accuracy.

Bagging, short for Bootstrap aggregating, involves training each tree on a random ________ of the training data.