Discuss CertNexus AIP-210 Exam Topic 3 Question 62

You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power. Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?

ADeep learning neural network

BRandom forest

CRidge regression

DSupport-vector machine

Suggested Answer: C

Ridge regression is a type of linear regression that adds a regularization term to the loss function to reduce overfitting and improve generalization. Ridge regression is fast and requires low memory and low processing power, as it only involves solving a system of linear equations. Ridge regression can also handle multicollinearity (high correlation among predictors) by shrinking the coefficients of correlated predictors.

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14 comments · newest first

Robt 5 days ago

Ridge regression is definitely faster and lighter. Good for quick predictions!

Suzan 10 days ago

SVM could work, but it might need more processing power.

Heike 15 days ago

Random forest is great, but it can be heavy on resources.

Coral 20 days ago

Agreed! Low memory usage is key for our project.

Annmarie 25 days ago

I think Ridge regression is the best choice. It's simple and efficient.

Ilda 1 month ago

I agree, ridge regression seems like the best fit here.

Gerald 1 month ago

Wait, are we sure SVM is low on resources?

Shaniqua 1 month ago

Definitely not deep learning for low memory!

Merissa 2 months ago

I think random forest might be too heavy for this.

Golda 2 months ago

Ridge regression is usually lightweight.

Howard 2 months ago

Support-vector machines can be efficient, but I recall they might struggle with larger datasets. I wonder if ridge regression is still the best fit.

Lezlie 2 months ago

Random forests can be quite powerful, but they can also be memory-heavy. I think I need to double-check their resource requirements.

Johnetta 2 months ago

I feel like ridge regression might be a good option since it's simpler and less resource-intensive, but I'm not entirely sure.

Adell 2 months ago

I remember discussing that deep learning models usually require a lot of resources, so I don't think that's the right choice here.

CertNexus AIP-210 Exam - Topic 3 Question 62 Discussion