Databricks Certified Professional Data Scientist Exam - Topic 4 Question 77 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist exam

Question #: 77
Topic #: 4

[All Databricks Certified Professional Data Scientist Questions]

You are working on a problem where you have to predict whether the claim is done valid or not. And you find that most of the claims which are having spelling errors as well as corrections in the manually filled claim forms compare to the honest claims. Which of the following technique is suitable to find out whether the claim is valid or not?

ANaive Bayes

BLogistic Regression

CRandom Decision Forests

DAny one of the above
In this problem you have been given high-dimensional independent variables like texts, corrections, test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
Support vector machines Naive Bayes Logistic regression Random decision forests

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Suggested Answer: D

by Jerilyn at Jan 12, 2025, 09:24 AM

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Corinne

4 months ago

All of them can be applied, but results may vary.

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Janine

5 months ago

Wait, can any of these really work for such messy data?

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Mozelle

5 months ago

Random Decision Forests could handle the complexity well.

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Ozell

5 months ago

I think Logistic Regression might be better for this.

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Flo

5 months ago

Naive Bayes is great for text classification!

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Judy

5 months ago

I recall a practice question where we used multiple techniques, so maybe option D is the safest bet since it covers all bases.

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Barbra

6 months ago

Random Decision Forests could handle the complexity of the data, but I wonder if it might overfit with so many variables.

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Stevie

6 months ago

I think Logistic Regression might be suitable since it deals with binary outcomes, but I need to double-check the assumptions.

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Brock

6 months ago

I remember we discussed how Naive Bayes works well with text data, but I'm not sure if it's the best choice here.

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Rossana

6 months ago

Okay, I think I've got a handle on this. Based on the information provided, I'd say that any of the techniques listed could potentially work well. I'd probably start with Naive Bayes since it's often a good baseline for text-based classification problems.

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Donette

6 months ago

Hmm, this is an interesting one. Given the high-dimensional nature of the data, I think Random Decision Forests could be a good approach. The ability to handle both text and structured data could be really useful here.

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Franklyn

6 months ago

I'm a bit confused by the wording of the question. It seems like we have a mix of text data (spelling errors) and structured data (corrections). I'm not sure which technique would be best - maybe I'd try a few different models and see which performs the best.

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Javier

6 months ago

This seems like a classic binary classification problem, so I'd probably start by trying a Logistic Regression model. The spelling errors and corrections in the claim forms could be good predictive features.

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Claudio

11 months ago

I'm feeling a bit 'naive' about this whole situation. But hey, at least I'm not trying to 'logistically' get away with something. Time to 'random forest' the heck out of this problem!

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Charolette

11 months ago

I'd say, 'Any one of the above' is the way to go. They're all powerful techniques, and the key is choosing the one that fits your data best. Though, I do wonder if they have a 'Sniff-out-Fraud-O-Matic' algorithm... that would be the real winner here!

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Kimi

9 months ago

D) Any one of the above

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Beatriz

10 months ago

C) Random Decision Forests

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Alisha

10 months ago

B) Logistic Regression

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Lizbeth

10 months ago

A) Naive Bayes

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Coleen

11 months ago

Naive Bayes, hands down! It's simple, yet effective, and can easily handle the text data in the claims. Plus, it's probably the most 'honest' algorithm for this honest-claims-versus-dishonest-claims problem.

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Terrilyn

10 months ago

C) Random Decision Forests

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Diego

10 months ago

D) Any one of the above

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Josephine

11 months ago

A) Naive Bayes

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Abraham

11 months ago

Random Decision Forests, all the way! It can handle high-dimensional data and is robust to outliers. Plus, the random nature of the forests helps capture the unpredictability of fraud.

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