Discuss Databricks Databricks Machine Learning Associate Exam Topic 3 Question 31

A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.

As a result, they have the following code block:

Which of the following changes do they need to make to the above code block in order to accomplish the task?

AChange SparkTrials() to Trials()

BReduce num_evals to be less than 10

CChange fmin() to fmax()

DRemove the trials=trials argument

ERemove the algo=tpe.suggest argument

Suggested Answer: A

The SparkTrials() is used to distribute trials of hyperparameter tuning across a Spark cluster. If the environment does not support Spark or if the user prefers not to use distributed computing for this purpose, switching to Trials() would be appropriate. Trials() is the standard class for managing search trials in Hyperopt but does not distribute the computation. If the user is encountering issues with SparkTrials() possibly due to an unsupported configuration or an error in the cluster setup, using Trials() can be a suitable change for running the optimization locally or in a non-distributed manner.

Reference

Hyperopt documentation: http://hyperopt.github.io/hyperopt/

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28 comments · newest first

Karrie 10 months ago

D? Really? Removing trials sounds risky!

Carey 10 months ago

C makes no sense, fmin is what you want for minimization.

Beth 10 months ago

A is definitely wrong, SparkTrials is needed!

Ethan 10 months ago

I think B is too restrictive, 10 should be fine.

Eleonora 10 months ago

E is unnecessary, algo=tpe.suggest is important for tuning.

Ettie 11 months ago

I vaguely recall that removing the trials=trials argument could lead to issues with tracking the trials, but I’m not completely sure.

Magnolia 11 months ago

I feel like we had a similar question about fmin() and fmax() in our practice tests. I think fmin() is used for minimization, so changing it to fmax() might not be the right move.

Gail 11 months ago

I'm a bit unsure, but I think reducing num_evals to less than 10 could limit the search space too much. We usually want more evaluations, right?

Sherly 11 months ago

I remember we discussed the importance of using SparkTrials for distributed hyperparameter tuning, so I think changing it to Trials() might not be correct.

Jamal 12 months ago

This seems straightforward enough. I'll carefully analyze the code and the provided options to determine the most effective change to make in order to accomplish the task.

Alease 12 months ago

I've worked with similar hyperparameter tuning problems before, so I think I have a good handle on this. My initial instinct is that option A, changing SparkTrials() to Trials(), might be the right approach.

Monroe 12 months ago

I'm a bit confused by the options here. I'll need to review the documentation on the Hyperopt library and Spark ML to determine the most appropriate changes to make.

Reita 1 year ago

Okay, let me think this through step-by-step. The key seems to be understanding the differences between the Trials and SparkTrials classes and how they impact the hyperparameter tuning process.

Carin 1 year ago

Hmm, this looks like a tricky one. I'll need to carefully read through the code and understand the context to figure out the best approach.

Felton 1 year ago

This is a tricky one, but I reckon the answer is E. Removing the algo=tpe.suggest argument could help the model tune the hyperparameters better. Besides, who needs algorithms when you've got pure, unadulterated luck? *winks*

Jerrod 1 year ago

Hmm, I'm not so sure. Changing fmin() to fmax() seems a bit odd. I'm leaning towards D - removing the trials=trials argument might be the way to go.

Beatriz 1 year ago

No way, B is wrong! We need to keep a decent number of evaluations to find the best hyperparameters. I'd go with A - changing SparkTrials() to Trials() seems like the right move here.

Major 1 year ago

↳ replying to Beatriz

I agree, we shouldn't reduce the number of evaluations. A is the best choice.

Ilene 1 year ago

↳ replying to Beatriz

I think A is the way to go. Changing SparkTrials() to Trials() makes sense.

Gaston 1 year ago

That's a good point, removing unnecessary arguments could simplify the code

Rasheeda 1 year ago

I think the answer is D) Remove the trials=trials argument because it seems unnecessary

Shelia 1 year ago

But wouldn't changing SparkTrials() to Trials() make more sense for tuning hyperparameters?

Gaston 1 year ago

I disagree, I believe the answer is B) Reduce num_evals to be less than 10

Melda 1 year ago

I think the correct answer is B. Reducing num_evals to less than 10 will help optimize the hyperparameters more efficiently.

Lajuana 1 year ago

↳ replying to Melda

C) Change fmin() to fmax()

Dulce 1 year ago

↳ replying to Melda

B) Reduce num_evals to be less than 10

Mila 1 year ago

↳ replying to Melda

A) Change SparkTrials() to Trials()

Shelia 1 year ago

I think the answer is A) Change SparkTrials() to Trials()

Databricks Machine Learning Associate Exam - Topic 3 Question 31 Discussion