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Which metric is commonly used to evaluate the performance of a Decision Tree?
Which metric is commonly used to evaluate the performance of a Decision Tree?
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Which metric is commonly used to evaluate the performance of a Decision Tree?
Mean Squared Error.
Accuracy.
F1 Score.
Confusion Matrix.
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Accuracy is a common metric used to evaluate the performance of a Decision Tree, especially in classification tasks.
Questions & Step-by-step Solutions
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Q: Which metric is commonly used to evaluate the performance of a Decision Tree?
Solution:
Accuracy is a common metric used to evaluate the performance of a Decision Tree, especially in classification tasks.
Steps: 5
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Step 1: Understand what a Decision Tree is. It is a model used to make decisions based on data.
Step 2: Know that when we use a Decision Tree for classification tasks, we want to see how well it predicts the correct categories.
Step 3: Learn about metrics, which are ways to measure performance. One common metric is accuracy.
Step 4: Accuracy tells us the percentage of correct predictions made by the Decision Tree compared to the total predictions.
Step 5: Conclude that accuracy is a widely used metric to evaluate how well a Decision Tree performs in classification tasks.
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