Decision Trees: A Comprehensive Guide to Tree-Based Predictions

Question 1

Which of the following is NOT a type of Decision Tree?

Accepted Answer

Rule Set

Answer

Classification Tree

Answer

Regression Tree

Answer

Random Forest

Question 2

In a decision tree, a node that signifies no further division is referred to as a

Accepted Answer

Leaf node

Answer

Root node

Answer

Internal node

Answer

Decision node

Question 3

Which metric assesses the overall performance of a decision tree, considering both precision and recall?

Accepted Answer

F1-score

Answer

Accuracy

Answer

Precision

Answer

Recall

Question 4

When handling numerical features in decision trees, which technique involves creating ranges within the feature values?

Accepted Answer

Binning

Answer

Clustering

Answer

Normalization

Answer

Discretization

Question 5

Cost-Complexity Pruning is a technique used to prevent

Accepted Answer

Overfitting

Answer

Underfitting

Answer

High bias

Answer

Low variance

Question 6

Which factor is NOT considered a potential limitation of decision trees?

Accepted Answer

Robustness to noisy data

Answer

Susceptibility to overfitting

Answer

Inability to handle large datasets

Answer

Difficulty in selecting optimal hyperparameters

Question 7

The process of dividing a node into multiple subnodes within a decision tree is termed

Accepted Answer

Splitting

Answer

Partitioning

Answer

Pruning

Answer

Expansion

Question 8

Random Forest is an ensemble method that leverages the collective power of

Accepted Answer

Multiple decision trees

Answer

Support Vector Machines

Answer

Neural Networks

Answer

Linear Regression models

Question 9

In Decision Tree construction, which of the following is NOT utilized as a criterion for selecting the optimal feature to split on?

Accepted Answer

Coefficient of Variation

Answer

Information Gain

Answer

Gini Impurity

Answer

Chi-square test

Question 10

In Decision Trees, what's the main reason for calculating entropy?

Accepted Answer

To measure the uncertainty in a dataset

Answer

To find the best split for a feature

Answer

To calculate the tree's depth

Question 11

Which of the following is not a typical impurity measure used in Decision Tree construction?

Accepted Answer

Standard Error of the Mean

Answer

Information Gain

Answer

Entropy

Answer

Gini Impurity

Question 12

Which of the following is a hyperparameter associated with Decision Trees?

Accepted Answer

Maximum Depth

Answer

Regularization Coefficient

Answer

Learning Rate

Answer

Kernel Type

Question 13

What is a primary advantage of Decision Trees compared to other machine learning algorithms?

Accepted Answer

High interpretability

Answer

Superior scalability

Answer

Inherent high bias

Answer

Exceptional low variance

Question 14

Which of the following is a common application scenario for Decision Trees?

Accepted Answer

Customer Segmentation

Answer

Natural Language Processing

Answer

Image Classification

Answer

Time Series Forecasting

Question 15

What is the purpose of pruning in Decision Trees?

Accepted Answer

Reduce overfitting

Answer

Enhance interpretability

Answer

Increase prediction accuracy

Answer

Accelerate training

Question 16

Which of the following ensemble methods commonly incorporates Decision Trees?

Accepted Answer

Random Forests

Answer

Linear Regression

Answer

Neural Networks

Answer

Support Vector Machines

Question 17

What is the primary criterion for determining the best split in a Decision Tree?

Accepted Answer

Minimizing impurity

Answer

Minimizing variance

Answer

Increasing tree depth

Answer

Maximizing classification accuracy

Question 18

Which of the following is a potential challenge or drawback associated with Decision Trees?

Accepted Answer

Overfitting

Answer

High computational requirements

Answer

Poor performance on continuous data

Answer

Inability to handle missing data

Question 19

What is the name of the process of transforming a continuous feature into a categorical feature for use in Decision Trees?

Accepted Answer

Binning

Answer

Scaling

Answer

Normalization

Answer

Encoding

Question 20

Which of the following measures quantifies the importance of a feature in a Decision Tree?

Accepted Answer

Gini importance

Answer

R-squared

Answer

AUC

Answer

F-score

Question 21

What is the significance of the root node in a Decision Tree?

Accepted Answer

It represents the feature that best splits the data into subsets with the greatest information gain or reduction in impurity.

Answer

It determines the maximum depth of the tree.

Answer

It always contains the most frequent class label.

Question 22

What type of data is most appropriate for Decision Tree algorithms?

Accepted Answer

Both categorical and numerical data

Answer

Numerical data only

Answer

Categorical data only

Answer

Textual data only

Question 23

What is the process of selecting the optimal feature for splitting a node in a Decision Tree called?

Accepted Answer

Feature selection or attribute selection

Answer

Splitting

Answer

Training

Answer

Pruning

Question 24

What is a key advantage of using Decision Trees?

Accepted Answer

Their simplicity and interpretability, making them easy to understand and visualize.

Answer

They require minimal data preprocessing.

Answer

They always produce the most accurate predictions.

Answer

They can handle extremely large datasets efficiently.

Question 25

What is the purpose of pruning in Decision Trees?

Accepted Answer

To reduce overfitting and improve generalization performance by removing unnecessary branches from the tree.

Answer

To increase the depth of the tree.

Answer

To improve accuracy on the training data.

Question 26

What is a potential disadvantage of Decision Trees?

Accepted Answer

They can be susceptible to overfitting, especially when the tree is not properly pruned.

Answer

They cannot handle missing values.

Answer

They require a vast amount of training data.

Answer

They are difficult to implement.

Question 27

What is the fundamental difference between classification and regression Decision Trees?

Accepted Answer

Classification trees predict discrete class labels, while regression trees predict continuous numerical values.

Answer

Classification trees are always deeper than regression trees.

Answer

Regression trees require more training data than classification trees.

Question 28

Which of the following is a hyperparameter that can be tuned in Decision Trees?

Accepted Answer

Maximum depth or minimum samples per leaf

Answer

Learning rate

Answer

Momentum

Answer

Batch size

Question 29

What is the primary purpose of pruning in decision tree learning?

Accepted Answer

Reduce overfitting

Answer

Enhance interpretability

Answer

Increase accuracy on unseen data

Question 30

Which of the following is an advantage of decision trees over regression models?

Accepted Answer

They can handle both categorical and continuous features without additional preprocessing

Answer

They are always more accurate

Answer

They require less data to train

Question 31

What is the distinction between a decision node and a leaf node in a decision tree?

Accepted Answer

A decision node evaluates a feature to split the data, while a leaf node assigns a final prediction.

Answer

A leaf node is always lower in the tree than a decision node.

Answer

A decision node has multiple branches, while a leaf node has only one.

Question 32

Which of the following techniques can be used to address missing values in decision tree learning?

Accepted Answer

Imputation

Answer

Bagging

Answer

Boosting

Answer

Pruning

Question 33

What is the primary purpose of a decision tree within the realm of machine learning?

Accepted Answer

To make predictions or classifications based on features of the input data.

Answer

To visualize the distribution of data points in a dataset.

Answer

To exclusively identify anomalies in a dataset.

Question 34

Among the algorithms below, which is widely employed for splitting nodes within a decision tree?

Accepted Answer

ID3 (Iterative Dichotomiser 3)

Answer

K-Means Clustering

Answer

Linear Regression

Answer

K-Nearest Neighbors (KNN)

Question 35

In the context of constructing a decision tree, what is the role of entropy?

Accepted Answer

To quantify the impurity or disorder within a data set.

Answer

To manage missing values in the dataset.

Answer

To determine the optimal number of leaf nodes in the tree.

Question 36

How do decision trees handle continuous features during the node splitting process?

Accepted Answer

Identifying thresholds that segment the data according to intervals.

Answer

Ignoring continuous features and focusing solely on categorical features.

Answer

Converting continuous features into categorical features.

Question 37

In the context of decision trees, what is the significance of 'pruning'?

Accepted Answer

Reducing the size and intricacy of the tree to prevent overfitting.

Answer

Expanding the depth of the tree to enhance accuracy.

Answer

Balancing data distribution within the tree's leaf nodes.

Question 38

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

Accepted Answer

Susceptibility to overfitting, particularly when dealing with noisy or redundant data.

Answer

Excessive computational cost during training and evaluation.

Answer

Inability to process large and complex datasets.

Question 39

Explain the primary distinction between classification trees and regression trees.

Accepted Answer

Classification trees predict discrete categories, while regression trees predict continuous numerical values.

Answer

Classification trees have a fixed depth, while regression trees can grow indefinitely.

Answer

Classification trees use supervised learning, while regression trees use unsupervised learning.

Question 40

Within a decision tree, what function does a leaf node fulfill?

Accepted Answer

Represents the final prediction or decision.

Answer

Measures the impurity or disorder of the data.

Answer

Serves as an input feature for decision-making.

Answer

Acts as a splitting criterion for dividing data.

Question 41

Describe how decision trees can be employed for feature selection.

Accepted Answer

By identifying features that appear higher in the tree structure, which typically indicates their greater significance for making predictions.

Answer

By discarding features that exhibit low correlation with the target variable.

Question 42

Which of the evaluation metrics listed below is generally not employed for assessing decision trees?

Accepted Answer

Mean Squared Error (MSE)

Answer

Recall

Answer

Accuracy

Answer

Precision