Feature Crosses:
Feature crosses combine multiple features into a single feature that captures the interaction between them. For location data, a feature cross of latitude and longitude can capture spatial dependencies that affect housing prices.
This approach allows the neural network to learn complex patterns related to geographic location 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 network and can improve the model's ability to learn from the data.
This method ensures that the model can leverage the combined information from both latitude and longitude in a meaningful way.
Model Training:
Using a numeric column for the feature cross helps in regularizing the model and prevents overfitting, which is crucial for achieving good generalization on unseen data.
Reference:
To engineer a feature that incorporates the physical dependency of location on housing prices for a neural network, creating a numeric column from a feature cross of latitude and longitude is the most effective approach. Here's why option B is the best choice: