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Databricks Machine Learning Associate Exam - Topic 1 Question 21 Discussion

Which of the following statements describes a Spark ML estimator?
D) An estimator is an alqorithm which can be fit on a DataFrame to produce a Transformer
A) An estimator is a hyperparameter arid that can be used to train a model
B) An estimator chains multiple alqorithms toqether to specify an ML workflow
C) An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions
E) An estimator is an evaluation tool to assess to the quality of a model

Databricks Machine Learning Associate Exam - Topic 1 Question 21 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam
Question #: 21
Topic #: 1
[All Databricks Machine Learning Associate Questions]

Which of the following statements describes a Spark ML estimator?

Show Suggested Answer Hide Answer
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


by Becky at Nov 14, 2024, 11:24 AM

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Nobuko
7 months ago
I’m surprised by how many people mix up estimators and transformers!
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Barb
8 months ago
I thought estimators were just for hyperparameters, not models.
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Malcolm
8 months ago
Wait, isn't an estimator just a trained model?
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Felicitas
8 months ago
Totally agree, D is the right choice!
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Kattie
8 months ago
An estimator can be fit on a DataFrame to produce a Transformer.
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Galen
9 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.
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Chantay
9 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.
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Brynn
9 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.
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Lottie
9 months 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.
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Yuette
9 months 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.
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Shakira
9 months 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.
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Gayla
9 months 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.
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Glendora
9 months 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.
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Paris
9 months 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.
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Shawn
2 years ago
Hmm, that makes sense too. I guess we'll have to review the material again to be sure.
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Sabra
2 years ago
I disagree, I believe the answer is D. It mentions fitting an algorithm on a DataFrame to produce a Transformer.
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Shawn
2 years ago
I think the answer is B, because it mentions chaining multiple algorithms together.
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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!
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Lonny
2 years ago
D) An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer
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Dante
2 years ago
B) An estimator chains multiple algorithms together to specify an ML workflow
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Yvonne
2 years ago
A) An estimator is a hyperparameter that can be used to train a model
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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!
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Franchesca
2 years ago
Jaime: Sounds good, let's go with D and see what happens.
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Armanda
2 years ago
User 3: I'm going with option D, it seems like a good choice.
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Jaime
2 years ago
User 2: I agree, let's rule out option A.
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Mickie
2 years ago
User 1: I think you're right, estimators are not hyperparameters.
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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.
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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.
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Ammie
2 years ago
I'm not sure, but option C also sounds plausible, turning features into predictions.
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Wava
2 years ago
I believe it's option A, an estimator being a hyperparameter makes sense to me.
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Anika
2 years ago
I think it might be option B, chaining multiple algorithms together sounds like what an estimator does.
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Derrick
2 years ago
I agree with you, option D is the correct answer.
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