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Question 165 - Professional Data Engineer discussion

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You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.

What should you do?

A.
Provide latitude and longtitude as input vectors to your neural net.
Answers
A.
Provide latitude and longtitude as input vectors to your neural net.
B.
Create a numeric column from a feature cross of latitude and longtitude.
Answers
B.
Create a numeric column from a feature cross of latitude and longtitude.
C.
Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.
Answers
C.
Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.
D.
Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
Answers
D.
Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
Suggested answer: B

Explanation:

Feature crosses combine multiple features into a single feature that captures the interactionbetween them. For location data, a feature cross of latitude and longitude can capture spatialdependencies that affect housing prices.This approach allows the neural network to learn complex patterns related to geographiclocation more effectively than using raw latitude and longitude values. Numerical Representation:Converting the feature cross into a numeric column simplifies the input for the neural networkand can improve the model's ability to learn from the data.This method ensures that the model can leverage the combined information from both latitudeand longitude in a meaningful way.Model Training:Using a numeric column for the feature cross helps in regularizing the model and preventsoverfitting, which is crucial for achieving good generalization on unseen data.To engineer a feature that incorporates the physical dependency of location on housing pricesfor a neural network, creating a numeric column from a feature cross of latitude and longitudeis the most effective approach. Here's why option B is the best choice

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asked 18/09/2024
Unai M
39 questions
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