Decision Trees: A Comprehensive Guide for Machine Learning

Question 1

Which primary criterion is used to split a node in a decision tree?

Accepted Answer

Information gain

Answer

Chi-square test result

Answer

Mean absolute error

Answer

Mean squared error

Question 2

What is the term for the maximum depth of a decision tree?

Accepted Answer

Tree height

Answer

Leaf count

Answer

Branch factor

Answer

Maximum level

Question 3

Which decision tree type is suitable for handling continuous target variables?

Accepted Answer

Regression tree

Answer

Classification tree

Answer

Binary tree

Answer

Ensemble tree

Question 4

What is a primary advantage of decision trees in machine learning?

Accepted Answer

High interpretability

Answer

Exceptional accuracy

Answer

Low computational cost

Answer

Robustness to noise

Question 5

What is the role of a leaf node in a decision tree?

Accepted Answer

Representing the final decision

Answer

Representing a split criterion

Answer

Representing a rule

Answer

Representing a feature

Question 6

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

Accepted Answer

Classification trees predict categories, while regression trees predict continuous values

Answer

Regression trees predict categories, while classification trees predict continuous values

Answer

Classification trees are non-parametric, while regression trees are parametric

Answer

Regression trees are non-parametric, while classification trees are parametric

Question 7

In decision tree construction, entropy measures the:

Accepted Answer

Impurity of a dataset

Answer

Quality of a feature split

Answer

Error rate of a model

Answer

Likelihood of an event occurring

Question 8

Which metric evaluates the effectiveness of a feature split by calculating the reduction in entropy?

Accepted Answer

Information gain

Answer

Gini impurity

Answer

Accuracy

Answer

F-score

Question 9

To mitigate overfitting in decision trees, which technique is employed?

Accepted Answer

Pruning

Answer

Early stopping

Answer

Ensemble methods

Answer

Regularization

Question 10

The primary objective of a decision tree is to:

Accepted Answer

Predict a target variable based on input features

Answer

Cluster data points

Answer

Identify patterns in data

Answer

Generate new features

Question 11

For regression tasks, which type of decision tree is suitable?

Accepted Answer

Regression tree

Answer

Classification tree

Answer

Decision stump

Question 12

Gini impurity is a measure of:

Accepted Answer

Node purity in decision trees

Answer

Classifier performance

Answer

Feature importance

Answer

Data distribution

Question 13

Which of the following is NOT a measure of impurity used in decision tree construction?

Accepted Answer

Accuracy

Answer

Entropy

Answer

Gini impurity

Answer

Information gain

Question 14

What is the main purpose of pruning in decision trees?

Accepted Answer

To reduce overfitting and improve generalization error

Answer

To increase the depth of the tree

Answer

To speed up the training process

Question 15

Which of the following is a widely used splitting criterion in decision tree algorithms?

Accepted Answer

Gini index

Answer

Precision-recall curve

Answer

Receiver Operating Characteristic (ROC) curve

Answer

Mean squared error

Question 16

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

Accepted Answer

The type of target variable being predicted

Answer

The shape of the tree

Answer

The construction algorithm

Question 17

What is a key advantage of using decision trees?

Accepted Answer

High interpretability and ease of understanding

Answer

High computational cost

Answer

Low accuracy compared to other machine learning algorithms

Answer

Sensitivity to outliers

Question 18

What is the primary role of information gain in decision tree construction?

Accepted Answer

To select the best feature to split on at each node

Answer

To calculate the impurity of a node

Answer

To determine the depth of the tree

Question 19

Which of the following is a type of pruning technique commonly used in decision trees?

Accepted Answer

Pre-pruning

Answer

Early stopping

Answer

Boosting

Answer

Bagging

Question 20

What is the main purpose of using cross-validation when evaluating decision tree models?

Accepted Answer

To estimate the generalization error and prevent overfitting

Answer

To increase the size of the training dataset

Answer

To simplify the model and improve interpretability

Question 21

Which of the following is a potential disadvantage of using decision trees?

Accepted Answer

Overfitting

Answer

High computational efficiency

Answer

Robustness to noise

Answer

Ability to handle non-linear relationships

Question 22

How does a random forest differ from a single decision tree?

Accepted Answer

A random forest is an ensemble of multiple decision trees trained on different subsets of the data

Answer

A decision tree is a supervised learning algorithm while a random forest is an unsupervised learning algorithm

Question 23

Which measure quantifies the uncertainty associated with a dataset?

Accepted Answer

Entropy

Answer

Standard deviation

Answer

Skewness

Answer

Variance

Question 24

In a decision tree, which attribute should be used as the primary splitting criterion to maximize information gain?

Accepted Answer

Attribute with the lowest entropy after the split

Answer

Attribute with the highest variance

Answer

Attribute with the most unique values

Answer

Attribute with the least missing values

Question 25

For regression decision trees, what is the most commonly used splitting criterion?

Accepted Answer

Mean squared error

Answer

Entropy

Answer

Information gain

Answer

Gini impurity

Question 26

Which of the following is considered a limitation of decision trees?

Accepted Answer

Susceptibility to overfitting

Answer

Robustness to noise

Answer

Interpretability

Answer

Bias toward numerical attributes

Question 27

What is the primary role of the root node in a decision tree?

Accepted Answer

Represents the entire dataset

Answer

Represents the most frequent class

Answer

Represents the most significant attribute

Answer

Represents the target variable

Question 28

What is a key advantage of using decision trees for feature selection?

Accepted Answer

Provides a visual representation of feature importance

Answer

Is immune to noise

Answer

Requires minimal data preparation

Answer

Can handle large datasets efficiently

Question 29

Which data types are generally suitable for decision tree analysis?

Accepted Answer

Both categorical and numerical

Answer

Numerical only

Answer

Categorical only

Answer

Textual only

Question 30

Which of the following is NOT a common application of decision trees?

Accepted Answer

Quantum computing

Answer

Medical diagnosis

Answer

Customer segmentation

Answer

Fraud detection

Question 31

In decision tree construction, which attribute is used to split a node?

Accepted Answer

Splitting Attribute

Answer

Decision Attribute

Answer

Primary Attribute

Answer

Majority Attribute

Question 32

What is the purpose of the root node in a decision tree?

Accepted Answer

To select the best splitting attribute that divides the data into the purest subsets.

Answer

To hold the entire dataset.

Answer

To represent the final prediction.

Question 33

What is the purpose of setting a depth limit when constructing a decision tree?

Accepted Answer

To prevent overfitting and improve generalization performance.

Answer

To ensure that the tree is balanced.

Answer

To reduce the computational complexity of tree construction.

Question 34

What is the key structural difference between a decision stump and a fully grown decision tree?

Accepted Answer

A decision stump has only one root node and one leaf node, while a fully grown decision tree has multiple levels and multiple leaf nodes.

Answer

A decision stump uses only numerical attributes, while a fully grown decision tree can use both numerical and categorical attributes.

Question 35

Which concept quantifies the randomness or impurity within a dataset, and is used as a measure to guide decision tree construction?

Accepted Answer

Entropy

Answer

Deviation

Answer

Skewness

Answer

Correlation

Question 36

What metric evaluates the effectiveness of a split in a decision tree by quantifying the reduction in entropy or impurity?

Accepted Answer

Information Gain

Answer

Mean Absolute Error

Answer

Gini Impurity

Answer

Cross-Entropy

Question 37

What is the maximum depth of a decision tree that has two levels of splits?

Accepted Answer

3

Answer

2

Answer

4

Question 38

Which of the following functions is NOT used as a split function in decision trees?

Accepted Answer

Linear Regression

Answer

Random Split

Answer

Categorical Split

Answer

Threshold

Question 39

Which ensemble method leverages multiple decision trees to improve predictive performance by combining their individual predictions?

Accepted Answer

Random Forest

Answer

Naive Bayes

Answer

Support Vector Machines

Answer

Gradient Boosting

Question 40

What is the main objective of a decision tree algorithm?

Accepted Answer

To construct a hierarchical structure that classifies data points based on their characteristics.

Answer

To cluster data points into groups with similar features.

Answer

To identify the optimal linear equation that separates different classes.

Answer

To perform dimensionality reduction on the data.

Question 41

Which of the following techniques can be used to evaluate the performance of a decision tree?

Accepted Answer

Cross-validation

Answer

Logistic regression

Answer

Principal component analysis

Answer

Linear regression

Question 42

What is a significant advantage of using decision trees over linear regression models?

Accepted Answer

Their ability to handle non-linear relationships and complex interactions.

Answer

Their superior accuracy in making predictions.

Answer

Their lower computational cost and faster training time.

Answer

Their ability to handle missing values more effectively.

Question 43

Which of the following is a potential limitation of decision trees?

Accepted Answer

Their susceptibility to overfitting and noisy data.

Answer

Their high computational cost.

Answer

Their inability to handle large datasets.

Answer

Their requirement for a predefined target variable.

Question 44

What is an ensemble method that can be used to enhance the performance of decision trees?

Accepted Answer

Random forest

Answer

K-nearest neighbors

Answer

Naive Bayes

Answer

Support vector machines

Question 45

Which measure quantifies the randomness or impurity of a dataset?

Accepted Answer

Entropy

Answer

Variance

Answer

Precision

Answer

Recall

Question 46

In decision tree construction, which metric determines the most impactful feature for splitting the data?

Accepted Answer

Information Gain

Answer

Root Mean Squared Error

Answer

Mean Absolute Error

Answer

Gini Impurity

Question 47

In a decision tree, what is a terminal node referred to as?

Accepted Answer

Leaf Node

Answer

Decision Node

Answer

Split Node

Answer

Root Node

Question 48

Which key strength of decision trees is directly related to their simplicity?

Accepted Answer

Easy to interpret and understand

Answer

Highly accurate on complex datasets

Answer

Require minimal data preprocessing

Answer

Capable of handling missing data

Question 49

What is the primary purpose of utilizing a validation set in decision tree pruning?

Accepted Answer

To evaluate the effectiveness of pruning and prevent overfitting

Answer

To guide the splitting criteria for each node

Answer

To select the optimal hyperparameters for the tree

Answer

To assist in determining the best features for constructing the tree

Question 50

Which decision tree variation is designed for predicting numerical values?

Accepted Answer

Regression Tree

Answer

Random Tree

Answer

Binary Tree

Answer

Classification Tree

Question 51

What is a potential drawback associated with decision trees?

Accepted Answer

Susceptible to overfitting without proper pruning

Answer

Inefficient on large datasets

Answer

Difficult to interpret and visualize

Answer

Inability to handle categorical features

Question 52

What is the purpose of entropy thresholding in decision tree construction?

Accepted Answer

To prevent nodes with insufficient instances

Answer

To select the most optimal splitting features

Answer

To determine the depth of the tree

Answer

To address missing data