Virtual Labs

In Scikit-learn, which class is used for Decision Tree classification?

a: DecisionTreeModel Explanation

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b: TreeClassifier Explanation

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c: DecisionTreeClassifier Explanation

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d: DTClassifier Explanation

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Using which parameter can we control overfitting in a Decision Tree?

a: n estimator Explanation

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b: Learning rate Explanation

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c: Batch size Explanation

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d: Max depth Explanation

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Which feature is used to reduce variance in Decision Trees?

a: Feature scaling Explanation

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b: Ensemble learning Explanation

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c: Data normalization Explanation

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d: Dimensionality reduction Explanation

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Which parameter controls the depth of a Decision Tree?

a: min samples split Explanation

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b: min samples leaf Explanation

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c: criterion Explanation

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d: max depth Explanation

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Which technique is used to improve Decision Tree stability?

a: Pruning Explanation

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b: Bagging Explanation

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c: Normalization Explanation

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d: Standardization Explanation

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What does a Decision Tree use to choose the first split at the root node?

a: Random selection of features Explanation

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b: Feature with maximum impurity reduction Explanation

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c: Feature with highest variance Explanation

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d: Feature with minimum depth Explanation

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Which parameter ensures that a leaf node has a minimum number of samples?

a: max depth Explanation

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b: min samples split Explanation

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c: min samples leaf Explanation

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d: n estimator Explanation

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Which property of Decision Trees makes them insensitive to feature scaling?

a: Greedy splitting strategy Explanation

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b: Use of threshold-based comparisons Explanation

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c: Use of gradient descent Explanation

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d: Dependence on distance metrics Explanation

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Which technique in Decision Trees helps to handle missing values in the dataset?

a: Ignoring rows with missing data Explanation

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b: Using surrogate splits Explanation

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c: Feature scaling Explanation

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d: Pruning Explanation

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How does increasing max_features in a Decision Tree affect splitting?

a: It restricts the number of features considered at each split Explanation

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b: It increases the maximum depth of the tree Explanation

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c: It changes the splitting criterion Explanation

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d: It guarantees higher accuracy Explanation

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