Q. How does Random Forest handle missing values?
-
A.
It cannot handle missing values
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B.
It ignores missing values completely
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C.
It uses imputation techniques
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D.
It can use surrogate splits
Solution
Random Forest can use surrogate splits to handle missing values, allowing it to make predictions even when some data is missing.
Correct Answer:
D
— It can use surrogate splits
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Q. In a Random Forest, what is the purpose of using multiple Decision Trees?
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A.
To increase the model's complexity
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B.
To reduce overfitting and improve accuracy
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C.
To simplify the model
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D.
To ensure all trees are identical
Solution
Using multiple Decision Trees in a Random Forest helps to reduce overfitting and improve the overall accuracy of the model.
Correct Answer:
B
— To reduce overfitting and improve accuracy
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Q. In which scenario would you prefer using a Random Forest over a single Decision Tree?
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A.
When interpretability is the main concern
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B.
When you have a small dataset
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C.
When you need higher accuracy and robustness
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D.
When computational resources are limited
Solution
Random Forests are preferred when higher accuracy and robustness are needed, especially in larger datasets.
Correct Answer:
C
— When you need higher accuracy and robustness
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Q. What does pruning refer to in the context of Decision Trees?
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A.
Adding more nodes to the tree
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B.
Removing nodes to reduce complexity
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C.
Increasing the depth of the tree
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D.
Changing the splitting criterion
Solution
Pruning refers to the process of removing nodes from a Decision Tree to reduce its complexity and prevent overfitting.
Correct Answer:
B
— Removing nodes to reduce complexity
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Q. What does the term 'bagging' refer to in the context of Random Forests?
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A.
Using a single Decision Tree for predictions
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B.
Combining predictions from multiple models
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C.
Randomly selecting features for each tree
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D.
Aggregating predictions by averaging
Solution
Bagging refers to combining predictions from multiple models to improve the overall performance and robustness of the model.
Correct Answer:
B
— Combining predictions from multiple models
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Q. What is a key feature of Random Forests that helps in feature selection?
-
A.
It uses all features for every tree
-
B.
It randomly selects a subset of features for each split
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C.
It eliminates all features with low variance
-
D.
It requires manual feature selection
Solution
Random Forests randomly select a subset of features for each split, which helps in feature selection and reduces overfitting.
Correct Answer:
B
— It randomly selects a subset of features for each split
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Q. What is a potential drawback of using Decision Trees?
-
A.
They are very fast to train
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B.
They can easily overfit the training data
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C.
They require no feature selection
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D.
They are not interpretable
Solution
A potential drawback of Decision Trees is that they can easily overfit the training data, especially if not properly pruned.
Correct Answer:
B
— They can easily overfit the training data
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Q. What is a primary advantage of using Decision Trees?
-
A.
They require a lot of data preprocessing
-
B.
They are easy to interpret and visualize
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C.
They always provide the best accuracy
-
D.
They cannot handle categorical data
Solution
Decision Trees are easy to interpret and visualize, making them user-friendly for understanding decision-making processes.
Correct Answer:
B
— They are easy to interpret and visualize
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Q. What is the main purpose of pruning in Decision Trees?
-
A.
To increase the depth of the tree
-
B.
To reduce the size of the tree and prevent overfitting
-
C.
To improve the interpretability of the tree
-
D.
To enhance the training speed
Solution
Pruning is used to reduce the size of the tree and prevent overfitting, leading to better generalization on unseen data.
Correct Answer:
B
— To reduce the size of the tree and prevent overfitting
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Q. What technique does Random Forest use to create diverse trees?
-
A.
Bagging
-
B.
Boosting
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C.
Stacking
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D.
Clustering
Solution
Random Forest uses bagging (Bootstrap Aggregating) to create diverse trees by training each tree on a random subset of the data.
Correct Answer:
A
— Bagging
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Q. Which evaluation metric is commonly used to assess the performance of a classification model like Decision Trees?
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A.
Mean Absolute Error
-
B.
Accuracy
-
C.
R-squared
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D.
Silhouette Score
Solution
Accuracy is a common evaluation metric used to assess the performance of classification models, including Decision Trees.
Correct Answer:
B
— Accuracy
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Q. Which of the following is a common criterion for splitting nodes in Decision Trees?
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A.
Mean Squared Error
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B.
Gini Impurity
-
C.
Euclidean Distance
-
D.
Cross-Entropy
Solution
Gini Impurity is a common criterion used for splitting nodes in Decision Trees to measure the impurity of a node.
Correct Answer:
B
— Gini Impurity
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Q. Which of the following statements is true about Decision Trees?
-
A.
They can only be used for regression tasks
-
B.
They can handle both categorical and numerical data
-
C.
They require normalization of data
-
D.
They are always the best choice for any dataset
Solution
Decision Trees can handle both categorical and numerical data, making them versatile for various types of datasets.
Correct Answer:
B
— They can handle both categorical and numerical data
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Q. Which of the following statements is true about Random Forests?
-
A.
They are always less accurate than a single Decision Tree
-
B.
They can only be used for regression tasks
-
C.
They improve accuracy by averaging multiple trees
-
D.
They require more computational resources than a single tree
Solution
Random Forests improve accuracy by averaging the predictions of multiple trees, which helps to reduce variance.
Correct Answer:
C
— They improve accuracy by averaging multiple trees
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