# What is the difference between a classification tree and a regression tree?

## What is the difference between a classification tree and a regression tree?

The primary difference between classification and regression decision trees is that, the classification decision trees are built with unordered values with dependent variables. The regression decision trees take ordered values with continuous values.

What is classification and regression tree analysis?

A Classification and Regression Tree(CART) is a predictive algorithm used in machine learning. It explains how a target variable’s values can be predicted based on other values. It is a decision tree where each fork is split in a predictor variable and each node at the end has a prediction for the target variable.

### What are the assumptions of decision tree?

Assumptions that we make while using the Decision tree: -Based on attribute values records are distributed recursively. -We use a statistical method for ordering attributes as a root node or the internal node. Entropy: Entropy is the measures of impurity, disorder, or uncertainty in a bunch of examples.

Can decision tree used for regression?

Decision Tree algorithm has become one of the most used machine learning algorithm both in competitions like Kaggle as well as in business environment. Decision Tree can be used both in classification and regression problem.

## What are the limitations of classification and regression trees?

Decision tree often involves higher time to train the model. Decision tree training is relatively expensive as the complexity and time has taken are more. The Decision Tree algorithm is inadequate for applying regression and predicting continuous values.

How classification and regression tree works and explain the advantages?

Classification and regression trees work to supply accurate predictions or predicted classifications, supported the set of if-else conditions. they typically have several advantages over regular decision trees. The interpretation of results summarised in classification or regression trees is typically fairly simple.

### What is the classification of a tree?

Phylogenetic classifications Trees are represented in each of the major groups of the vascular plants: pteridophytes (seedless vascular plants that include the tree ferns), gymnosperms (cycads, ginkgoes, and conifers), and angiosperms (flowering plants).

Does decision tree assume normality?

In Decision Tree as we have no probabilistic model, but just binary split, we don’t need to make any assumption at all.

## How decision tree is used for classification?

Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Leaf node (e.g., Play) represents a classification or decision.

How regression is performed using trees?

A regression tree is built through a process known as binary recursive partitioning, which is an iterative process that splits the data into partitions or branches, and then continues splitting each partition into smaller groups as the method moves up each branch.

### What are the disadvantages of classification and Regression Trees cart )?

Disadvantages of CART: A small change in the dataset can make the tree structure unstable which can cause variance. Decision tree learners create underfit trees if some classes are imbalanced. It is therefore recommended to balance the data set prior to fitting with the decision tree.