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

The user wants to specify a search space for hyperparameters and let the tuning process randomly select values.GridSearchCVsystematically tries every combination of the provided hyperparameter values, which can be computationally expensive and time-consuming.RandomizedSearchCV, on the other hand, samples hyperparameters from a distribution for a fixed number of iterations. This approach is usually faster and still can find very good parameters, especially when the search space is large or includes distributions.

References

• Scikit-Learn documentation on hyperparameter tuning: https://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization

When using theHyperoptlibrary withfmin, the goal is to find the minimum of the objective function. Since you are usingcross_val_scoreto calculate the R2 score which is a measure of the proportion of the variance for a dependent variable that's explained by an independent variable(s) in a regression model, higher values are better. However,fminseeks to minimize the objective function, so to align withfmin's goal, you should return the negative of the R2 score (-r2). This way, by minimizing the negative R2,fminis effectively maximizing the R2 score, which can lead to a more accurate model.

References

• Hyperopt Documentation: http://hyperopt.github.io/hyperopt/
• Scikit-Learn documentation on model evaluation: https://scikit-learn.org/stable/modules/model_evaluation.html

If the model object is passed to the estimator parameter ofCrossValidatorand the cross-validation object itself is placed as a stage in the pipeline, the feature engineering stages within the pipeline would be applied separately to each training and validation fold during cross-validation. This leads to a significant issue: the feature engineering stages would be computed using validation data, thereby leaking information from the validation set into the training process. This would potentially invalidate the cross-validation results by giving an overly optimistic performance estimate.References:

• Cross-validation and Pipeline Integration in MLlib (Avoiding Data Leakage in Pipelines).