Databricks Certified Machine Learning - Associate
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Explain how Spark's machine learning library (MLlib) handles the distribution and parallelization of linear regression models. Include details on how data is partitioned, how computations are distributed across nodes, and how the results are aggregated to form the final model.
Explain how Spark's machine learning library (MLlib) handles the distribution and parallelization of linear regression models. Include details on how data is partitioned, how computations are distributed across nodes, and how the results are aggregated to form the final model.
Simulated
Explanation:
Spark's MLlib handles the distribution and parallelization of linear regression models by partitioning the data across multiple nodes and distributing the computation of regression coefficients in parallel. The results from each node are then aggregated to form the final model, leveraging Spark's distributed computing capabilities to ensure efficient and scalable processing.