« Prev. This set of Artificial Intelligence Multiple Choice Questions & Answers (MCQs) focuses on “Decision Trees”. 1. A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. a) Decision tree.
What is decision tree in simple terms?
A decision tree is a graphical representation of all the possible solutions to a decision based on certain conditions . Tree models where the target variable can take a finite set of values are called classification trees and target variable can take continuous values (numbers) are called regression trees.
What is decision tree in Sanfoundry?
« Prev. This set of Artificial Intelligence Multiple Choice Questions & Answers (MCQs) focuses on “Decision Trees”. 1. A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. a) Decision tree.
What is decision tree explain?
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences , including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
What is decision tree F Mcq?
Decision Tree is a display of an algorithm . 3. Decision Tree is. a) Flow-Chart. b) Structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label.
What are the decision tree commonly used for?
In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making . As the name goes, it uses a tree-like model of decisions.
How do you represent decision nodes?
The decision nodes (branch and merge nodes) are represented by diamonds . The flows coming out of the decision node must have guard conditions (a logic expression between brackets).
What is decision tree explain with example?
Introduction Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. ... An example of a decision tree can be explained using above binary tree .
What is difference between decision tree and random forest?
A decision tree combines some decisions, whereas a random forest combines several decision trees . Thus, it is a long process, yet slow. Whereas, a decision tree is fast and operates easily on large data sets, especially the linear one. The random forest model needs rigorous training.
How a decision tree reaches its decision?
Explanation: A decision tree reaches its decision by performing a sequence of tests .
What are the types of decision tree?
There are 4 popular types of decision tree algorithms: ID3, CART (Classification and Regression Trees), Chi-Square and Reduction in Variance .
What is value in decision tree?
Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
How do you create a decision tree?
- Start with your overarching objective/ “big decision” at the top (root) ...
- Draw your arrows. ...
- Attach leaf nodes at the end of your branches. ...
- Determine the odds of success of each decision point. ...
- Evaluate risk vs reward.
What is the disadvantage of decision trees Mcq?
Apart from overfitting, Decision Trees also suffer from following disadvantages: 1. Tree structure prone to sampling – While Decision Trees are generally robust to outliers, due to their tendency to overfit, they are prone to sampling errors.
How will you counter Overfitting in the decision tree?
- Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set.
- Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree.
What is the final objective of decision tree?
As the goal of a decision tree is that it makes the optimal choice at the end of each node it needs an algorithm that is capable of doing just that . That algorithm is known as Hunt’s algorithm, which is both greedy, and recursive.
