What are the benefits and challenges of feature engineering for complex data sets?

Feature engineering transforms raw data into meaningful features that enhance model performance. It can uncover hidden patterns, leading to more accurate predictions and better insights. However, it requires domain expertise and can be time-consuming, especially for complex data sets.

Feature Engineering Brings several benefits- 1) Lower data requirements. 2) Can help you overcome power-law scaling. 3) Can guide your AI Model toward the properties desired. 4) Better performance. One paper I mention a lot to drill this home is “A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks” compared the performance of finetuned LLMs with a puny SVM for text classification in various specialized business contexts. SVM won

In some domains like radiation biology and oncology, useful features can be engineered based on mechanistically motivated mathematical models. They can be then potentially be used together with a variety of other features such as omics and image data to create better and more interpretable models.

1) Feature engineering is subjective and requires domain knowledge and creativity to identify relevant variables and transformations. 2)Data quality issues, such as missing values, outliers, and errors, can affect the quality of features and model performance. 3)Feature engineering can increase the risk of overfitting if the feature selection or transformation is too complex or specific to the training data. 4) The selected features and transformations should generalize well to new data and not introduce biases or overfitting. 5)Feature engineering may involve sensitive data, so privacy and security protocols must be followed to protect the data.

Look out for correlations/colinearities. Combining variables that have high such relationships can help you cut down on size. Feature Engineering is expensive, but it's a one-time expense. Deep Learning is a recurring expense, which shows up at every inference. When you implement feature engineering, you're applying your biases and assumptions to the domain. Always be cognizant of that. EDA and domain experts are your best friends. People put too much emphasis on the computational methods for finding features and not enough on the domain expertise of people that work in the field.