You may have heard the phrase “garbage in, garbage out,” and in the case of machine learning, this phrase holds profound significance. The quality and structure of the data fed into a model play a pivotal role in determining its performance. Feature engineering, the art and science of selecting, modifying, and creating the right input features, stands at the heart of this process. While it offers a pathway to enhance model accuracy and efficiency, it’s also riddled with potential pitfalls that can derail a project. This article explores the best practices that seasoned data scientists swear by and the common missteps that beginners often fall prey to.
Best Practices When Performing Feature Engineering for Machine Learning
Make sure to handle missing data in your input features
In a real-world case where a project aims to predict housing prices, not all data entries may have information about a house’s age. Instead of discarding these entries, you may impute the missing data by using a strategy like “mean imputation,” where the average value of the house’s age from the dataset is used. By correctly handling missing data instead of just discarding it, the model will have more data to learn from, which could lead to better model performance.
Use one-hot encoding for categorical data
For instance, if we have a feature “color” in a dataset about cars, with the possible values of “red,” “blue,” and “green,” we would transform this into three separate binary features: “is_red,” “is_blue,” and “is_green.” This strategy allows the model to correctly interpret categorical data, improving the quality of the model’s findings and predictions.
Consider feature scaling
As a real example, a dataset for predicting disease may have age in years (1100) and glucose level measurements (70180). Scaling places these two features on the same scale, allowing each to contribute equally to distance computations like in the K-nearest neighbors (KNN) algorithm. Feature scaling may improve the performance of many machine learning algorithms, rendering them more efficient and reducing computation time.
Create interaction features where relevant
An example could include predicting house price interactions, which may be beneficial. Creating a new feature that multiplies the number of bathrooms by the total square footage may give the model valuable new information. Interaction features can capture patterns in the data that linear models otherwise wouldn’t see, potentially improving model performance.
Remove irrelevant features
In a problem where we need to predict the price of a smartphone, the color of the smartphone may have little impact on the prediction and can be dropped. Removing irrelevant features can simplify your model, make it faster, more interpretable, and reduce the risk of overfitting.
What to Watch Out for When Performing Feature Engineering
Ignoring irrelevant features
This could result in a model with poor predictive performance, as irrelevant features don’t contribute to the output and might even add noise to the data. The mistake is caused by a lack of understanding and analysis of the relationship between different datasets and the target variable.
- Imagine a business that wants to use machine learning to predict monthly sales. They input data such as employee count and office size, which have no relationship with sales volume.
Fix: Avoid this by conducting a thorough feature analysis to understand which data variables are necessary and remove those that are not.
Overfitting from too many features
The model may have perfect performance on training data (because it has effectively ‘memorized’ the data) but may perform poorly on new, unseen data. This is known as overfitting. This mistake is usually due to the misconception that “more is better.” Adding too many features to the model can lead to large complexity, making the model harder to interpret.
- Consider an app forecasting future user growth that inputs 100 features into their model, but most of them share overlapping information.
Fix: Counter this by using strategies like dimensionality reduction and feature selection to minimize the number of inputs, thus reducing the model complexity.
Not normalizing features
The algorithm may give more weight to features with a larger scale, which can lead to inaccurate predictions. This mistake often happens due to a lack of understanding of how machine learning algorithms work. Most algorithms perform better if all features are on a similar scale.
- Imagine a healthcare provider uses patient age and income level to predict the risk of a certain disease but doesn’t normalize these features, which have different scales.
Fix: Apply feature scaling techniques to bring all the variables into a similar scale to avoid this issue.
Neglecting to handle missing values
Models can behave unpredictably when confronted with missing values, sometimes leading to faulty predictions. This pitfall often happens because of an oversight or the assumption that the presence of missing values won’t adversely affect the model.
- For example, an online retailer predicting customer churn uses purchase history data but does not address instances where purchase data is absent.
Fix: Implement strategies to deal with missing values, such as data imputation, where you replace missing values with statistical estimates.
Case Study: Engineering Features for a School Project
Meet Sarah, a high school junior with a passion for technology. For her science fair project, Sarah decided to create a machine learning model to predict the popularity of songs based on various attributes. She had access to a dataset containing details about different songs, such as tempo, genre, and lyrical themes. Sarah knew that simply feeding this raw data into a machine learning model wouldn’t yield the best results. She had heard about the importance of feature engineering and decided to apply best practices to her project.
Sarah began by understanding the nature of her dataset. She identified continuous data like tempo and categorical data like genre. She realized that each type of data required a different approach for feature engineering.
While the dataset had a ‘genre’ column, Sarah decided to break it down further. For instance, she created binary features like ‘is_rock,’ ‘is_pop,’ and ‘is_jazz.’ For tempo, she categorized songs into ‘slow,’ ‘medium,’ and ‘fast’ based on their beats per minute. Sarah noticed that some features, like song duration, varied widely. She used the StandardScaler from sci-kit-learn to ensure all her features had a similar scale, ensuring no single feature would unduly influence the model.
While examining song durations, Sarah found a few songs with extremely long durations, likely errors in the dataset. Instead of removing them, she decided to clip these outliers to a maximum value, ensuring they didn’t skew her model’s training. Sarah used one-hot encoding for genres, turning them into binary columns. This way, her model could easily understand and use the genre information without making incorrect assumptions about their ordinal nature.
Some songs lacked information on certain attributes. Instead of discarding these songs, Sarah used the median value of that feature to fill in the gaps, ensuring her dataset remained robust and comprehensive. Before finalizing her features, Sarah used a decision tree to evaluate the importance of each feature. She found that some of her newly engineered features, like ‘is_rock,’ were more influential than the original ‘genre’ column. This validated her efforts in feature engineering.
Sarah resisted the temptation to engineer too many features, knowing that this could lead to overfitting, where her model would perform well on her dataset but poorly on new, unseen data. She was cautious not to use future information. For instance, she didn’t use the number of downloads in the next month as a feature to predict the song’s popularity in the current month.
Sarah also ensured that she didn’t inadvertently introduce bias into her model by over-relying on one category or genre.