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Which of the following machine learning algorithms typically uses bagging?
Suggested Answer: C
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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
41 comments · newest first
TH
Thurman
10 months ago
Nope, K-means is clustering, not bagging.
LE
Leslee
11 months ago
Wait, K-means doesn't use bagging? That's surprising!
LA
Lashon
11 months ago
I thought decision trees could use bagging too?
TE
Tequila
11 months ago
Definitely C, it's a classic.
LO
Long
11 months ago
Random forest uses bagging!
RI
Rickie
12 months ago
I feel like gradient boosted trees are more about boosting than bagging, so I would lean towards random forests here.
WI
Willie
12 months ago
K-means doesn't sound right for bagging, but I can't recall if decision trees do.
VA
Valene
12 months ago
I remember practicing with random forests, and I think they definitely use bagging.
SH
Sherita
1 year ago
I think bagging is mostly associated with ensemble methods, but I'm not completely sure which ones specifically use it.
MA
Man
1 year ago
I remember learning that bagging is commonly used with decision trees, so I'll go with option D.
SH
Sherell
1 year 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.
MA
Maurine
1 year ago
Hmm, I'm a bit confused on the difference between bagging and boosting. I'll have to think this through carefully.
MA
Magdalene
1 year ago
I'm pretty sure random forest uses bagging, so I'll go with option C.
LO
Louvenia
1 year ago
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ES
Essie
1 year ago
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SH
Sherman
1 year ago
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YO
Yong
1 year ago
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NA
Nan
1 year ago
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LU
Luann
2 years ago
C. Random forest. Bagging is the secret sauce that makes it so darn accurate.
CL
Clay
2 years ago
D. Linear regression? What is this, a comedy show? Random forest all the way, baby!
NG
Nguyet
2 years ago
E) Decision tree
JA
Jannette
2 years ago
C) Random forest
ST
Stefan
2 years ago
Definitely not Linear regression, that's for sure!
ME
Merissa
2 years ago
I agree, Random forest is great for using bagging.
CH
Charlesetta
2 years ago
Random forest all the way! It's a powerful algorithm.
LO
Lore
2 years ago
A) Gradient boosted trees
CA
Catalina
2 years ago
I believe Random forest is the correct answer because it combines multiple decision trees through bagging.
EL
Elenor
2 years ago
This is easy, it's C. Random forest. Bagging is like the icing on the cake for that algorithm.
RE
Renay
2 years ago
I'm not sure, but I think A) Gradient boosted trees also uses bagging.
CE
Cecil
2 years ago
Hmm, I'm going with C. Random forest. Gotta love those trees, am I right?
EL
Elli
2 years ago
Yeah, Random forest is known for its use of bagging.
AR
Aracelis
2 years ago
Random forest is definitely a popular choice for bagging.
OD
Odelia
2 years ago
Random forest is definitely the way to go for that!
DE
Delisa
2 years ago
I agree, Random forest is a great choice for bagging.
MA
Margery
2 years ago
I think Random forest is the way to go too.
DO
Dottie
2 years ago
I agree, Random forest is a great choice.
SE
Serita
2 years ago
I agree with Gracia, Random forest uses bagging to improve accuracy.
GR
Gracia
2 years ago
I think the answer is C) Random forest.
CA
Casandra
2 years ago
Random forest, for sure! Bagging is like a machine learning superpower.
GE
Gerardo
2 years ago
I agree, bagging really gives random forest an edge in machine learning.
LU
Luisa
2 years ago
Random forest is definitely the one that uses bagging. It's so powerful!