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Databricks Exam Databricks Machine Learning Associate Topic 3 Question 9 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam
Question #: 9
Topic #: 3
[All Databricks Machine Learning Associate Questions]

Which of the following machine learning algorithms typically uses bagging?

Show Suggested Answer Hide Answer
Suggested Answer: C

Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.


Databricks documentation on Random Forest: Random Forest in Spark ML

Contribute your Thoughts:

Tequila
2 days ago
Definitely C, it's a classic.
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Long
8 days ago
Random forest uses bagging!
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Rickie
14 days ago
I feel like gradient boosted trees are more about boosting than bagging, so I would lean towards random forests here.
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Willie
19 days ago
K-means doesn't sound right for bagging, but I can't recall if decision trees do.
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Valene
24 days ago
I remember practicing with random forests, and I think they definitely use bagging.
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Sherita
1 month ago
I think bagging is mostly associated with ensemble methods, but I'm not completely sure which ones specifically use it.
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Man
1 month ago
I remember learning that bagging is commonly used with decision trees, so I'll go with option D.
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Sherell
1 month ago
I know bagging is a technique that combines multiple models, so I'm thinking it's most likely used with random forest or gradient boosted trees. I'll go with C.
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Maurine
1 month ago
Hmm, I'm a bit confused on the difference between bagging and boosting. I'll have to think this through carefully.
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Magdalene
1 month ago
I'm pretty sure random forest uses bagging, so I'll go with option C.
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Louvenia
1 month ago
Okay, let's break this down step-by-step. The key is ensuring the traffic is encrypted end-to-end, so we'll need to configure both CloudFront and the backend to use HTTPS.
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Essie
1 month ago
I think "client" and "standalone" might be the right choices since we talked a lot about server types in class.
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Sherman
1 month ago
This looks like a straightforward question about interpreting a utilization chart. I'll carefully review the chart options and choose the one that best matches the description.
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Yong
1 month ago
Collaborating with the sales team could also be a key step. They often have insights on why specific solutions were picked.
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Nan
1 month ago
Okay, I think the key here is to take ownership of the issue and work collaboratively with the customer. Engaging the service delivery manager and offering free consultation seems like a good way to demonstrate our commitment to resolving the problem.
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Luann
1 year ago
C. Random forest. Bagging is the secret sauce that makes it so darn accurate.
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Clay
1 year ago
D. Linear regression? What is this, a comedy show? Random forest all the way, baby!
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Nguyet
1 year ago
E) Decision tree
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Jannette
1 year ago
C) Random forest
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Stefan
1 year ago
Definitely not Linear regression, that's for sure!
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Merissa
1 year ago
I agree, Random forest is great for using bagging.
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Charlesetta
1 year ago
Random forest all the way! It's a powerful algorithm.
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Lore
1 year ago
A) Gradient boosted trees
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Catalina
1 year ago
I believe Random forest is the correct answer because it combines multiple decision trees through bagging.
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Elenor
1 year ago
This is easy, it's C. Random forest. Bagging is like the icing on the cake for that algorithm.
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Renay
1 year ago
I'm not sure, but I think A) Gradient boosted trees also uses bagging.
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Cecil
1 year ago
Hmm, I'm going with C. Random forest. Gotta love those trees, am I right?
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Elli
1 year ago
Yeah, Random forest is known for its use of bagging.
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Aracelis
1 year ago
Random forest is definitely a popular choice for bagging.
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Odelia
1 year ago
Random forest is definitely the way to go for that!
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Delisa
1 year ago
I agree, Random forest is a great choice for bagging.
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Margery
1 year ago
I think Random forest is the way to go too.
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Dottie
1 year ago
I agree, Random forest is a great choice.
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Serita
1 year ago
I agree with Gracia, Random forest uses bagging to improve accuracy.
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Gracia
1 year ago
I think the answer is C) Random forest.
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Casandra
1 year ago
Random forest, for sure! Bagging is like a machine learning superpower.
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Gerardo
1 year ago
I agree, bagging really gives random forest an edge in machine learning.
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Luisa
1 year ago
Random forest is definitely the one that uses bagging. It's so powerful!
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