Discuss Databricks Databricks Machine Learning Associate Exam Topic 1 Question 21

Which of the following statements describes a Spark ML estimator?

AAn estimator is a hyperparameter arid that can be used to train a model

BAn estimator chains multiple alqorithms toqether to specify an ML workflow

CAn estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions

DAn estimator is an alqorithm which can be fit on a DataFrame to produce a Transformer

EAn estimator is an evaluation tool to assess to the quality of a model

Suggested Answer: D

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.

Reference

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

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32 comments · newest first

Nobuko 10 months ago

I’m surprised by how many people mix up estimators and transformers!

Barb 11 months ago

I thought estimators were just for hyperparameters, not models.

Malcolm 11 months ago

Wait, isn't an estimator just a trained model?

Felicitas 11 months ago

Totally agree, D is the right choice!

Kattie 11 months ago

An estimator can be fit on a DataFrame to produce a Transformer.

Galen 12 months ago

I vaguely recall that an estimator is used to train a model, but I can't remember if it directly produces predictions or if that's the role of a transformer.

Chantay 12 months ago

I’m a bit confused about the definitions. I thought an estimator was more about the model itself rather than just an algorithm.

Brynn 12 months ago

I remember practicing a question about how estimators and transformers work together. I feel like option D might be the right choice since it mentions fitting on a DataFrame.

Lottie 1 year ago

I think an estimator is something that can be fit on a DataFrame, but I'm not sure if it's just a hyperparameter or something else.

Yuette 1 year ago

Okay, I remember learning about estimators in class. An estimator is an algorithm that can be trained on data to produce a model. That matches option D, so that's my answer.

Shakira 1 year ago

I'm not entirely sure about this. The options seem to describe different aspects of Spark ML, but I'm not sure which one specifically defines an estimator. I'll have to guess and hope for the best.

Gayla 1 year ago

I'm pretty confident about this one. An estimator is an algorithm that can be fit on data to produce a model, which is then used as a Transformer to make predictions. I'm going with option D.

Glendora 1 year ago

Hmm, I'm a bit confused by the different options. I'll need to think this through carefully. Maybe I should review my notes on Spark ML estimators before answering.

Paris 1 year ago

This question seems straightforward. I think the correct answer is D - an estimator is an algorithm that can be fit on a DataFrame to produce a Transformer.

Shawn 2 years ago

Hmm, that makes sense too. I guess we'll have to review the material again to be sure.

Sabra 2 years ago

I disagree, I believe the answer is D. It mentions fitting an algorithm on a DataFrame to produce a Transformer.

Shawn 2 years ago

I think the answer is B, because it mentions chaining multiple algorithms together.

Vashti 2 years ago

Ha, this question is a real spark-ler! Get it? Spark? Anyway, I think D is the way to go. Estimators are like the superheroes of Spark ML, turning DataFrames into Transformers with a single bound!

Lonny 2 years ago

↳ replying to Vashti

D) An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer

Dante 2 years ago

↳ replying to Vashti

B) An estimator chains multiple algorithms together to specify an ML workflow

Yvonne 2 years ago

↳ replying to Vashti

A) An estimator is a hyperparameter that can be used to train a model

Evangelina 2 years ago

Hmm, I'm not sure about this one. But I know that estimators are definitely not hyperparameters, so I can rule out option A. Maybe I'll just go with D and hope for the best!

Franchesca 2 years ago

↳ replying to Evangelina

Jaime: Sounds good, let's go with D and see what happens.

Armanda 2 years ago

↳ replying to Evangelina

User 3: I'm going with option D, it seems like a good choice.

Jaime 2 years ago

↳ replying to Evangelina

User 2: I agree, let's rule out option A.

Mickie 2 years ago

↳ replying to Evangelina

User 1: I think you're right, estimators are not hyperparameters.

Margurite 2 years ago

Option C seems like the right choice. An estimator is a trained ML model that can take a DataFrame with features and output a DataFrame with predictions.

Dacia 2 years ago

I think option D is the correct answer. An estimator is an algorithm that can be fit on a DataFrame to produce a Transformer, which then can be used to transform new data.

Ammie 2 years ago

↳ replying to Dacia

I'm not sure, but option C also sounds plausible, turning features into predictions.

Wava 2 years ago

↳ replying to Dacia

I believe it's option A, an estimator being a hyperparameter makes sense to me.

Anika 2 years ago

↳ replying to Dacia

I think it might be option B, chaining multiple algorithms together sounds like what an estimator does.

Derrick 2 years ago

↳ replying to Dacia

I agree with you, option D is the correct answer.

Databricks Machine Learning Associate Exam - Topic 1 Question 21 Discussion