Databricks Certified Machine Learning - Associate
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In the context of exploratory data analysis (EDA) within Databricks, which tool or library is most appropriate for visualizing the distribution of a numerical feature across various categories?
Explanation:
The Databricks Display function stands out as the optimal choice for creating visualizations in Databricks notebooks during EDA. It offers a variety of chart types and integrates seamlessly with the Databricks environment, facilitating an efficient exploration of data distributions. While Matplotlib is a versatile Python library for plotting, the Display function's integration with Databricks enhances the user experience. MLlib CrossValidator is unrelated to visualization, focusing instead on hyperparameter tuning in Spark MLlib. Databricks Delta, while beneficial for data storage and management, does not serve as a visualization tool. Thus, for visualizing numerical feature distributions across categories in Databricks, the Display function is the most suitable option.