What does feature engineering involve?

Feature engineering involves the process of selecting, modifying, or creating new variables (features) from raw data that can improve the performance of machine learning models. This process is crucial because the quality and relevance of features can significantly impact the ability of models to learn from data and make accurate predictions.

Selecting variables involves identifying which features from the existing dataset are most relevant to the predictive task at hand. This can include dropping irrelevant features or those that are redundant.

Transforming variables can include various techniques such as scaling, normalizing, encoding categorical variables, or creating new features through mathematical operations or aggregations. For example, combining date features into a single 'day of the week' variable can sometimes help capture patterns that are not evident in the raw data.

While creating new algorithms, building neural networks, and conducting performance evaluations are important aspects of developing AI solutions, they are not directly related to the concept of feature engineering. Feature engineering specifically focuses on optimizing the input data features to enhance model training and accuracy.