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

Ensemble learning is a machine learning technique that involves combining several models to solve a particular problem. The scenario described fits the concept of ensemble learning, where two models, each performing well under different conditions, are combined to create a more robust model. This approach often leads to better performance as it combines the strengths of multiple models.

References

• Introduction to Ensemble Learning: https://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/

To calculate the overall cross-validation root-mean-squared error (RMSE), you average the RMSE values obtained from each validation fold. Given the RMSE values of 10.0, 12.0, and 17.0 for the three folds, the overall cross-validation RMSE is calculated as the average of these three values:

Overall CV RMSE=10.0+12.0+17.03=39.03=13.0Overall CV RMSE=310.0+12.0+17.0​=339.0​=13.0

Thus, the correct answer is 13.0, which accurately represents the average RMSE across all folds.References:

• Cross-validation in Regression (Understanding Cross-Validation Metrics).

To filter rows in a Spark DataFrame based on a condition, you use thefiltermethod along with a column condition. The correct syntax in PySpark to accomplish this task isspark_df.filter(col("price") > 0), which filters the DataFrame to include only those rows where the value in the "price" column is greater than 0. Thecolfunction is used to specify column-based operations. The other options provided either do not use correct Spark DataFrame syntax or are intended for different types of data manipulation frameworks like pandas.References:

• PySpark DataFrame API documentation (Filtering DataFrames).