Databricks Databricks-Machine-Learning-Associate Exam With Confidence Using Practice Dumps

In the context of Spark MLlib, an estimator refers to an algorithm which can be "fit" on a DataFrame to produce a model (referred to as a Transformer), which can then be used to transform one DataFrame into another, typically adding predictions or model scores. This is a fundamental concept in machine learning pipelines in Spark, where the workflow includes fitting estimators to data to produce transformers.

References

• Spark MLlib Documentation:https://spark.apache.org/docs/latest/ml-pipeline.html#estimators

To organize MLflow runs with one parent run for the tuning process and a child run for each unique combination of hyperparameter values, the data scientist can specifynested=Truewhen starting the child run. This approach ensures that each child run is properly nested under the parent run, maintaining a clear hierarchical structure for the experiment. This nesting helps in tracking and comparing different hyperparameter combinations within the same tuning process.References:

• MLflow Documentation (Managing Nested Runs).

The Root Mean Squared Error (RMSE) is a standard and widely used metric for evaluating the accuracy of regression models. The statement that it is invalid is incorrect. Here’s a breakdown of why the other statements are or are not valid:

• Transformations and RMSE Calculation:If the model predictions were transformed (e.g., using log), they should be converted back to their original scale before calculating RMSE to ensure accuracy in the evaluation. Missteps in this conversion process can lead to misleading RMSE values.
• Accuracy of Models:Without additional information, we can't definitively say which model is more accurate without considering their RMSE values properly scaled back to the original price scale.
• Appropriateness of RMSE:RMSE is entirely valid for regression problems as it provides a measure of how accurately a model predicts the outcome, expressed in the same units as the dependent variable.

References

• "Applied Predictive Modeling" by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the chapters discussing model evaluation metrics.