BDA 2

In a decision tree, each internal node represents a (a)   .

The purpose of the (a)   in a decision tree is to classify or predict the target variable.

A decision tree is constructed by recursively partitioning the data based on the (a)   that maximizes information gain or Gini impurity reduction.

The process of selecting the best feature to split the data at each node involves evaluating criteria such as (a)   .

K-means is a widely used (a)   algorithm.

K-means partitions data into (a)   clusters.

Each cluster in K-means is represented by its (a)   .

The K-means algorithm aims to minimize the (a)   .

K-means is commonly employed in (a)   to identify customer segments.

Image compression often utilizes (a)   for reducing file sizes.