Q. How do Decision Trees handle categorical variables?
-
A.
By converting them to numerical values
-
B.
By creating binary splits
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C.
By ignoring them
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D.
By using one-hot encoding
Solution
Decision Trees create binary splits based on the categories of the categorical variables.
Correct Answer:
B
— By creating binary splits
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Q. How do Decision Trees handle missing values?
-
A.
They cannot handle missing values
-
B.
By ignoring them completely
-
C.
By using surrogate splits
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D.
By imputing values with the mean
Solution
Decision Trees can use surrogate splits to handle missing values effectively.
Correct Answer:
C
— By using surrogate splits
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Q. In which application would you use Decision Trees for customer segmentation?
-
A.
Predicting customer churn
-
B.
Recommending products
-
C.
Analyzing website traffic
-
D.
Optimizing supply chain logistics
Solution
Decision Trees can be used to predict customer churn by segmenting customers based on their behavior.
Correct Answer:
A
— Predicting customer churn
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Q. In which scenario would Random Forests be preferred over a single Decision Tree?
-
A.
When interpretability is the main goal
-
B.
When the dataset is small
-
C.
When overfitting is a concern
-
D.
When the model needs to run in real-time
Solution
Random Forests reduce overfitting by averaging multiple Decision Trees, making them more robust.
Correct Answer:
C
— When overfitting is a concern
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Q. In which scenario would Random Forests be preferred over Decision Trees?
-
A.
When interpretability is crucial
-
B.
When the dataset is small
-
C.
When overfitting is a concern
-
D.
When the model needs to be simple
Solution
Random Forests reduce overfitting by averaging multiple Decision Trees, making them more robust.
Correct Answer:
C
— When overfitting is a concern
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Q. What is a common application of Decision Trees in the healthcare industry?
-
A.
Predicting patient outcomes
-
B.
Image recognition
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C.
Natural language processing
-
D.
Time series forecasting
Solution
Decision Trees are often used to predict patient outcomes based on various health metrics.
Correct Answer:
A
— Predicting patient outcomes
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Q. What is a common use of Decision Trees in finance?
-
A.
Predicting stock prices
-
B.
Customer segmentation
-
C.
Fraud detection
-
D.
Market trend analysis
Solution
Decision Trees are frequently used in fraud detection to classify transactions as legitimate or fraudulent.
Correct Answer:
C
— Fraud detection
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Q. What is a key advantage of using ensemble methods like Random Forests?
-
A.
They are simpler to implement
-
B.
They reduce variance and improve accuracy
-
C.
They require less computational power
-
D.
They are always more interpretable
Solution
Ensemble methods like Random Forests reduce variance and improve accuracy by combining multiple models.
Correct Answer:
B
— They reduce variance and improve accuracy
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Q. What is a limitation of Decision Trees?
-
A.
They are very interpretable
-
B.
They can easily overfit the training data
-
C.
They handle both categorical and numerical data
-
D.
They require a lot of data to train
Solution
Decision Trees can easily overfit the training data, especially with complex trees.
Correct Answer:
B
— They can easily overfit the training data
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Q. What is the primary purpose of using Random Forests in machine learning?
-
A.
To increase model interpretability
-
B.
To reduce variance and improve accuracy
-
C.
To simplify the model
-
D.
To eliminate the need for feature selection
Solution
Random Forests aim to reduce variance and improve accuracy by averaging multiple Decision Trees.
Correct Answer:
B
— To reduce variance and improve accuracy
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Q. What type of data is best suited for Decision Trees?
-
A.
Unstructured data
-
B.
Categorical and numerical data
-
C.
Time series data
-
D.
Text data
Solution
Decision Trees can handle both categorical and numerical data effectively.
Correct Answer:
B
— Categorical and numerical data
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Q. Which application is NOT typically associated with Random Forests?
-
A.
Credit scoring
-
B.
Spam detection
-
C.
Image classification
-
D.
Linear regression
Solution
Random Forests are primarily used for classification and regression tasks, not linear regression.
Correct Answer:
D
— Linear regression
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Q. Which evaluation metric is commonly used to assess the performance of Decision Trees?
-
A.
Mean Squared Error
-
B.
Accuracy
-
C.
Silhouette Score
-
D.
F1 Score
Solution
Accuracy is a common metric for evaluating the performance of classification models like Decision Trees.
Correct Answer:
B
— Accuracy
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Q. Which metric is commonly used to evaluate the performance of Decision Trees?
-
A.
Mean Squared Error
-
B.
Accuracy
-
C.
Silhouette Score
-
D.
F1 Score
Solution
Accuracy is a common metric for evaluating the performance of classification Decision Trees.
Correct Answer:
B
— Accuracy
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Q. Which of the following is a benefit of using Random Forests in classification tasks?
-
A.
They are always faster than Decision Trees
-
B.
They provide feature importance scores
-
C.
They require less data preprocessing
-
D.
They are easier to visualize
Solution
Random Forests can provide insights into feature importance, helping to understand which features are most influential.
Correct Answer:
B
— They provide feature importance scores
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Q. Which of the following is a key advantage of using Random Forests?
-
A.
They are easier to interpret than Decision Trees
-
B.
They can handle missing values well
-
C.
They require less computational power
-
D.
They always outperform Decision Trees
Solution
Random Forests can handle missing values effectively by using multiple trees to fill in gaps.
Correct Answer:
B
— They can handle missing values well
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