Feature Engineering｜Coding Crossroads

Safety by Design Expert’s Note

For safety experts, feature engineering is a critical process in developing robust and fair AI systems.

Carefully crafted features can help mitigate biases, improve model interpretability, and enhance the overall safety of AI applications.

Introduction

In this section, we focus on creating new features and interaction terms to enhance our dataset for the “House Prices – Advanced Regression Techniques” challenge. We’ll use the FeatureEngineer class to implement these transformations.

You can find the complete code for the data engineering process in my GitHub repository.

New Features

Total Square Footage (TotalSF): Sum of TotalBsmtSF, 1stFlrSF, and 2ndFlrSF
House Age: The age of the house at the time of sale
Time Since Last Remodel: Years since the house was last remodeled
Total Bathrooms: Sum of all bathroom-related features
Is New House: Boolean indicating if the house is new (built in the year of sale)
Has Pool: Boolean indicating if the house has a pool
Total Porch SF: Sum of all porch-related square footages
Overall House Condition: Interaction between OverallQual and OverallCond

Interaction Features

TotalSF * OverallQual
GrLivArea * TotRmsAbvGrd
HouseAge * OverallQual
GarageArea * GarageCars
YearBuilt * YearRemodAdd
TotalSF * HouseAge
1stFlrSF * 2ndFlrSF
LotArea * Neighborhood
TotalSF * OverallCond
GrLivArea * Neighborhood

Explanation of New Features

We create new features that capture additional aspects of the data, providing the model with more information to improve its predictive power.

Here’s a breakdown of the new features we’ve engineered:

Total Square Footage (TotalSF): This feature represents the total square footage of a house by summing up the TotalBsmtSF (total basement square footage), 1stFlrSF (first floor square footage), and 2ndFlrSF (second floor square footage). By aggregating these areas, TotalSF provides a comprehensive measure of the house’s overall size.

Safety Implication: This feature provides a standardized measure of house size, reducing potential biases related to how different cultures or regions might value various living spaces differently.

Why it matters: Without this aggregate feature, the model might overemphasize certain types of square footage (e.g., basement vs. above-ground), potentially leading to biased predictions in diverse housing markets.

House Age: This feature calculates the age of the house at the time of sale by subtracting the YearBuilt from the year of sale (YrSold).

Time Since Last Remodel: This feature captures the years since the house was last remodeled. It reflects the likelihood that the house has modern features or updates, which can significantly affect its market value.

Safety Implication: These features help prevent age-related discrimination while still capturing relevant information about the property’s condition.

Why it matters: Relying solely on the year a house was built could lead to unfair devaluation of older properties that have been well-maintained or recently renovated.

Total Bathrooms: This feature is the sum of all bathroom-related features. The total number of bathrooms is often a significant factor in a home’s appeal and functionality, directly influencing its price.

Safety Implication: This aggregate feature helps normalize across different cultural norms for bathroom configurations.

Why it matters: Different cultures may have varying preferences for full baths vs. half baths. Using an aggregate feature reduces the risk of the model developing biases based on culturally specific bathroom configurations.

Is New House: This boolean feature indicates whether the house was built in the year it was sold. A value of True signifies a new construction, which is generally associated with a premium in the market.

Safety Implication: These boolean features help isolate specific property attributes, reducing the risk of these factors unduly influencing predictions for properties without these features.

Why it matters: Without these explicit features, the model might implicitly overvalue newness or pool presence, potentially leading to unfair predictions for older homes or those without pools.

Has Pool: This boolean feature indicates the presence of a pool (PoolArea > 0). Homes with pools are often more valuable, especially in certain climates or luxury markets.

Total Porch SF: This feature sums up the square footage of all porch-related features. Porches contribute to the overall livable space and can enhance the attractiveness of the property.

Overall House Condition: This interaction feature combines OverallQual (overall material and finish quality) and OverallCond (overall condition rating) to provide a comprehensive measure of the house’s quality and condition. This feature is particularly useful in capturing the combined effect of quality and condition on the house’s price.

Safety Implication: This interaction feature provides a more holistic view of a property’s state, reducing the risk of oversimplification.

Why it matters: Relying on quality or condition alone could lead to biased predictions. For example, a high-quality house in poor condition might be unfairly valued.

Explanation of Interaction Features

Interaction features are created by combining two or more existing features to capture the interdependencies and multiplicative effects that may not be apparent when considering individual features alone.

These interactions can provide the model with deeper insights into the relationships between different aspects of a house, leading to better predictive performance. Below are the interaction features we’ve engineered:

TotalSF * OverallQual: This interaction between the total square footage of the house and the overall quality rating helps capture the idea that larger homes with higher quality finishes are more valuable, amplifying the effect of size on price based on quality.

GrLivArea * TotRmsAbvGrd: This feature combines the ground living area (GrLivArea) with the total number of rooms above ground (TotRmsAbvGrd). It reflects how the amount of living space interacts with the layout and room count, which can impact the home’s livability and, consequently, its value.

HouseAge * OverallQual: This interaction captures the relationship between the age of the house and its overall quality. It helps to highlight how well older houses have maintained their quality or how newer homes might be perceived based on their construction quality.

GarageArea * GarageCars: This feature combines the garage area (GarageArea) with the number of cars the garage can accommodate (GarageCars). It emphasizes the functionality and utility of the garage, which can be a key selling point for certain buyers.

YearBuilt * YearRemodAdd: This interaction reflects the relationship between the original construction year (YearBuilt) and the year of the last remodel (YearRemodAdd). It helps to identify homes that may have significant upgrades or changes over time, potentially increasing their value.

TotalSF * HouseAge: This feature combines the total square footage with the age of the house. It captures how the size of the house interacts with its age, possibly indicating how well larger homes retain their value over time.

1stFlrSF * 2ndFlrSF: This interaction between the first floor and second floor square footage helps to capture the balance or imbalance between the two levels, which might affect the home’s overall appeal and functionality.

LotArea * Neighborhood: This feature interacts the lot area with the neighborhood, reflecting how the value of land is influenced by its location. In some neighborhoods, larger lots may command a premium, while in others, the effect might be less pronounced.

TotalSF * OverallCond: This interaction combines the total square footage with the overall condition of the house, emphasizing how the size of the house and its state of repair together influence its market value.

GrLivArea * Neighborhood: This interaction considers the ground living area and the neighborhood, highlighting how the livable space within a house is perceived differently depending on its location.

These interaction features allow the model to capture more complex relationships within the data, leading to potentially better predictions by considering how certain aspects of a home amplify or mitigate the effects of others.

Safety Implication: These features capture complex relationships, helping to prevent oversimplification that could lead to biased predictions.

Why it matters: Without these interactions, the model might not capture how the value of certain features (e.g., square footage) can vary based on other factors (e.g., quality, neighborhood), potentially leading to unfair predictions.

Conclusion

Feature engineering is a powerful technique that can significantly improve the performance of machine learning models. By creating new features and transforming existing features, we’ve prepared the Kaggle House Prices dataset for effective modeling.

In the next section, we will discuss feature selection.