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    What are common pitfalls when interpreting feature importance in tree-based models?

    Asked on Thursday, Dec 11, 2025

    Interpreting feature importance in tree-based models, such as those generated by decision trees, random forests, or gradient boosting, can be misleading if not done carefully. These models often provi…

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    What are the key differences between K-means and DBSCAN clustering methods?

    Asked on Wednesday, Dec 10, 2025

    K-means and DBSCAN are both popular clustering algorithms used in data science, but they differ significantly in their approach and application. K-means is a centroid-based algorithm that partitions t…

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    How can I handle imbalanced classes in a classification problem effectively?

    Asked on Tuesday, Dec 09, 2025

    Handling imbalanced classes in a classification problem is crucial for building robust models that generalize well. Techniques such as resampling, using different evaluation metrics, and employing spe…

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    How do you handle missing data in a dataset when preparing it for machine learning?

    Asked on Monday, Dec 08, 2025

    Handling missing data is a critical step in preparing a dataset for machine learning, as it can impact model performance and accuracy. Common techniques include imputation, removal, or using algorithm…

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