Decision Trees: A Comprehensive Guide for Machine Learning

Question 1

To construct a Decision Tree, what is the first step?

Accepted Answer

Prepare the data

Answer

Choose the best features

Answer

Assess the model's performance

Answer

Split the tree

Question 2

In machine learning, CART and ID3 are common Decision Tree types. Which type is better suited for a scenario where the dataset contains both numerical and categorical features? Explain your choice.

Accepted Answer

CART is more suitable because it can handle both numerical and categorical data, while ID3 can only handle categorical data.

Answer

ID3 is more suitable because it can handle numerical data, while CART can only handle categorical data.

Answer

CART and ID3 are equally suited for handling both numerical and categorical data.

Answer

Neither CART nor ID3 can handle both numerical and categorical data.

Question 3

Which of the following algorithms is NOT a type of ensemble method used for decision trees?

Accepted Answer

Isolation Forest

Answer

Gradient Boosting Machine

Answer

Random Forest

Answer

AdaBoost

Question 4

What is the purpose of "pre-pruning" in decision tree algorithms?

Accepted Answer

To limit the growth of the tree during its construction, reducing overfitting

Answer

To simplify the decision tree and improve generalization

Answer

To remove irrelevant features from the dataset

Question 5

Which of the following measures is used to evaluate the performance of a Decision Tree for classification tasks?

Accepted Answer

F1-score

Answer

Root Mean Squared Error (RMSE)

Answer

Adjusted R-squared

Answer

Mean Squared Error (MSE)

Question 6

What is the main benefit of using hyperparameter tuning for decision trees?

Accepted Answer

It enables optimization of the decision tree's parameters to improve performance on unseen data

Answer

It eliminates the need for feature scaling

Answer

It reduces the computational cost of training the decision tree

Question 7

Which of the following techniques can be used to reduce the variance of decision trees?

Accepted Answer

Bagging (Bootstrap Aggregating)

Answer

Principal Component Analysis (PCA)

Answer

Linear Regression

Answer

K-Means Clustering

Question 8

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

Accepted Answer

Regression trees predict continuous target values, while classification trees predict discrete target values

Answer

Regression trees use Gini impurity as a splitting criterion, while classification trees use Information Gain

Answer

Classification trees can handle non-linear relationships, while regression trees cannot

Question 9

Which of the following statements about decision tree induction algorithms is FALSE?

Accepted Answer

They always produce a single, optimal decision tree

Answer

They are greedy algorithms that make locally optimal choices at each node

Answer

They can handle both categorical and numerical features

Answer

They are susceptible to overfitting if not properly tuned

Question 10

What is the core concept behind the "Gain Ratio" attribute selection criterion?

Accepted Answer

Select the attribute that maximizes the ratio of Information Gain to Split Information

Answer

Select the attribute with the highest correlation to the target variable

Answer

Select the attribute with the lowest standard deviation

Question 11

What is the node in a Decision Tree that represents the entire dataset?

Accepted Answer

Root node

Answer

Split node

Answer

Internal node

Answer

Leaf node

Question 12

Which metric is specifically designed to measure the impurity of a dataset and is commonly used in Decision Trees?

Accepted Answer

Gini impurity

Answer

Mean squared error

Answer

F1 score

Answer

Accuracy

Question 13

Compared to other machine learning algorithms, what is a key advantage of Decision Trees?

Accepted Answer

Interpretability

Answer

Fast training time

Answer

High accuracy

Question 14

Which of the following factors can contribute to overfitting in Decision Trees?

Accepted Answer

Excessive tree depth

Answer

Balanced class distribution

Answer

Low feature count

Answer

Large dataset size

Question 15

What is the primary objective of pruning in Decision Trees?

Accepted Answer

Reduces tree complexity and improves generalization performance.

Answer

Handles missing values in the dataset.

Answer

Optimizes feature selection for better performance.

Answer

Increases tree depth and accuracy.

Question 16

Which of the following is a potential limitation of Decision Trees?

Accepted Answer

Sensitivity to noisy data and outliers

Answer

Inability to handle continuous variables

Answer

High computational cost

Answer

Requirement for a large dataset

Question 17

What is the role of a stopping criterion in Decision Trees?

Accepted Answer

To prevent overfitting by regulating tree growth.

Answer

To increase tree depth and accuracy.

Answer

To select the optimal features for splitting.

Answer

To handle missing values in the dataset.

Question 18

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

Accepted Answer

Random Forest

Answer

Regression Tree

Answer

Classification Tree

Answer

Decision Stump

Question 19

What is the process of recursively splitting a dataset into smaller subsets called nodes referred to as?

Accepted Answer

Tree Construction

Answer

Tree Prediction

Answer

Tree Evaluation

Answer

Tree Pruning

Question 20

What technique is used to address overfitting and enhance generalization in Decision Trees?

Accepted Answer

Pruning

Answer

Regularization

Answer

Early Stopping

Answer

Principal Component Analysis

Question 21

Which type of Decision Tree is used for making predictions on continuous target variables?

Accepted Answer

Regression Tree

Answer

Random Forest

Answer

Decision Stump

Answer

Classification Tree

Question 22

Which of the following is NOT a hyperparameter that influences the behaviour of a Decision Tree model?

Accepted Answer

Learning rate

Answer

Maximum depth

Answer

Minimum samples per leaf

Question 23

What algorithm is typically used to find the optimal split point in Decision Tree construction?

Accepted Answer

Greedy Algorithm

Answer

Exhaustive Search

Answer

Backtracking

Answer

Hill Climbing

Question 24

In a Decision Tree, a node denoting a class label is known as a:

Accepted Answer

Leaf node

Answer

Internal node

Answer

Root node

Answer

Split node

Question 25

Which of the following splitting criteria measures the reduction of entropy in the data?

Accepted Answer

Information gain

Answer

Gini impurity

Answer

Kolmogorov-Smirnov test

Answer

Chi-squared test

Question 26

To prevent overfitting and improve the generalization ability of Decision Trees, a technique known as ______ is commonly employed.

Accepted Answer

Pruning

Answer

Early stopping

Answer

Regularization

Answer

Cross-validation

Question 27

Which of the following is a potential disadvantage of Decision Trees?

Accepted Answer

Interpretability can be limited in complex trees

Answer

Can handle large datasets efficiently

Answer

Robust to missing values

Answer

Suitable for all types of data

Question 28

To improve the accuracy and robustness of Decision Trees, a technique known as _____ can be implemented.

Accepted Answer

Random Forests

Answer

Gradient Boosting

Answer

Support Vector Machines

Answer

Neural Networks

Question 29

Which of the following is NOT a commonly used criterion for selecting the best split point in a decision tree?

Accepted Answer

Number of leaves in the resulting subtrees

Answer

Gini impurity

Answer

Information gain

Answer

Entropy

Question 30

A decision tree is considered 'overfit' when it:

Accepted Answer

Performs well on the training data but poorly on new, unseen data

Answer

Is unable to classify any data points correctly

Answer

Has a very shallow structure with few nodes

Question 31

Which of these techniques can be used to prevent overfitting in decision trees?

Accepted Answer

Pruning

Answer

K-nearest neighbors

Answer

Linear regression

Answer

Boosting

Question 32

In a decision tree, what does a leaf node represent?

Accepted Answer

A prediction for the target variable

Answer

A feature used for splitting the data

Answer

A threshold value for a feature

Answer

The root node of the tree

Question 33

Which of the following is NOT a characteristic of decision trees?

Accepted Answer

They are highly sensitive to outliers in the data

Answer

They can handle both categorical and numerical features

Answer

They can be used for both classification and regression tasks

Answer

They are relatively easy to interpret

Question 34

You are building a decision tree to predict customer churn. Which of the following features would likely be most useful for a split near the root of the tree?

Accepted Answer

Average monthly spending

Answer

Preferred communication channel

Answer

Number of customer service calls

Question 35

Consider a decision tree where the target variable is 'Credit Approval' with values 'Approved' and 'Rejected'. What would be the most likely value at a leaf node if the path to that node involved high income and excellent credit score?

Accepted Answer

Approved

Answer

Rejected

Answer

Cannot be determined

Question 36

What is the primary advantage of using a decision tree over a linear regression model for a classification task?

Accepted Answer

Decision trees are more interpretable and can handle non-linear relationships in the data

Answer

Decision trees are computationally faster

Answer

Decision trees are less prone to overfitting

Question 37

How does the Gini impurity measure relate to the quality of a decision tree split?

Accepted Answer

A lower Gini impurity indicates a better split, where the resulting subsets are more homogeneous.

Answer

Gini impurity only measures the purity of the target variable, not the quality of the split

Answer

A higher Gini impurity indicates a better split, where the resulting subsets are more diverse

Question 38

Which of the following scenarios would be best suited for using a decision tree algorithm?

Accepted Answer

Predicting whether a customer will purchase a product based on their demographics and browsing history

Answer

Predicting the price of a house based on its size, location, and number of bedrooms