Virtual Labs

What is the learning type of Decision Tree?

a: Unsupervised Explanation

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b: Reinforcement Explanation

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c: Supervised Explanation

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d: Semi-supervised Explanation

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Which node represents the final output in a Decision Tree?

a: Root node Explanation

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b: Internal node Explanation

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c: Parent node Explanation

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d: Leaf node Explanation

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What is the effect of using too many features for splitting?

a: The tree always becomes more accurate Explanation

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b: The tree may become unstable Explanation

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c: The algorithm stops training Explanation

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d: The dataset becomes normalized automatically Explanation

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What happens if a Decision Tree grows too deep?

a: Underfitting Explanation

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b: Improved generalization Explanation

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c: Overfitting Explanation

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d: Reduced variance Explanation

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Which metric is commonly used to decide the best split in a Decision Tree classifier?

a: Mean Squared Error (MSE) Explanation

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b: Gini Index Explanation

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c: Euclidean Distance Explanation

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d: Accuracy Score Explanation

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What is the main reason Decision Trees are prone to overfitting?

a: They use ensemble techniques Explanation

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b: They create very deep trees that memorize training data Explanation

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c: They cannot handle missing values Explanation

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d: They require feature scaling Explanation

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Why is pruning used in a Decision Tree?

a: Reduce overfitting Explanation

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b: Reduce max depth Explanation

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c: Increase training time Explanation

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d: Increase max depth Explanation

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Which splitting criterion is based on information theory?

a: Gini Index Explanation

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b: Variance Explanation

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c: Entropy Explanation

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d: Euclidean Distance Explanation

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How does a Decision Tree handle categorical features?

a: It requires one-hot encoding for categorical variables Explanation

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b: It ignores categorical features Explanation

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c: It automatically converts them into numeric values Explanation

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d: It cannot process datasets with categorical features Explanation

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How does a Decision Tree treat continuous numerical features during splitting?

a: It ignores continuous features Explanation

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b: It converts them into categorical features automatically Explanation

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c: It finds the best threshold to split the data Explanation

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d: It requires scaling to split continuous features Explanation

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