A synthetic feature is a new variable created by transforming or combining existing features in a dataset, rather than being directly measured or collected. In machine learning and data science, synthetic features are engineered to help models uncover patterns, relationships, or distinctions in the data that might not be obvious from the raw input variables alone. This process is a key part of feature engineering, as synthetic features can boost the predictive power and interpretability of models.
There are many ways to generate synthetic features. One common approach is to combine two or more features mathematically, such as adding, subtracting, multiplying, or dividing them. For example, if you have features for height and weight, you might create a synthetic feature called Body Mass Index (BMI) by dividing weight by the square of height. Other techniques include aggregating data (like calculating averages over time), applying domain-specific formulas, or even using dimensionality reduction algorithms like Principal Component Analysis (PCA) to extract new features that capture important variance in the data.
Synthetic features can help address limitations in raw data. Sometimes, raw features are too granular, too noisy, or not directly useful for the problem at hand. By creating meaningful synthetic features, data scientists can highlight the underlying structure in the data, making it easier for algorithms to learn. For example, in a customer dataset, a synthetic feature like “years as a customer” (derived from account creation date and current date) might be more useful than just the account creation date alone.
In modern machine learning, especially with deep learning, models can sometimes learn useful synthetic features automatically within their hidden layers. However, for many types of data (especially tabular data), manual feature engineering and the creation of synthetic features are still powerful techniques that often lead to higher model performance.
It’s important to note that creating synthetic features requires a good understanding of both the data and the business or scientific context. Poorly chosen synthetic features can add noise, increase the risk of overfitting, or introduce biases. On the other hand, well-crafted synthetic features can simplify the modeling process and make models more robust and interpretable.
In summary, synthetic features are not present in the original dataset but are constructed from it to enrich the information available to machine learning models. They play a crucial role in improving model accuracy, especially in scenarios where the relationships between variables are complex or not directly represented in the data.
