Databricks Databricks-Machine-Learning-Associate Dumps Questions Answers

Tree of Parzen Estimators (TPE) is a sequential model-based optimization algorithm that selects hyperparameter values based on the outcomes of previous trials. It models the probability density of good and bad hyperparameter values and makes informed decisions about which hyperparameters to try next.

This approach contrasts with methods like random search and grid search, which do not use information from previous trials to guide the search process.

References:

• Hyperopt and TPE

The lack of improvement in model accuracy across evaluations suggests that the optimization algorithm might not be effectively exploring the hyperparameter space. Iterative optimization algorithms like Tree-structured Parzen Estimators (TPE) or Bayesian Optimization can adapt based on previous evaluations, guiding the search towards more promising regions of the hyperparameter space.

Changing the optimization algorithm can lead to better utilization of the information gathered during each evaluation, potentially improving the overall accuracy.

References:

• Hyperparameter Optimization with Hyperopt

The functionmapInPandasin the PySpark DataFrame API allows for applying a function to each partition of the DataFrame. When working with grouped data,groupbyfollowed byapplyInPandasis the correct approach to apply a function to each group as a separate Pandas DataFrame. However, if the function should apply across each partition of the grouped data rather than on each individual group,mapInPandaswould be utilized. Since the code snippet indicates the use ofgroupby, the intent seems to be to applytrain_modelon each group specifically, which aligns withapplyInPandas. Thus,applyInPandasis a better fit to ensure that each group generated bygroupbyis processed through thetrain_modelfunction, preserving the partitioning and grouping integrity.

References

• PySpark Documentation on applying functions to grouped data:https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.GroupedData.applyInPandas.html