Feature Engineering Techniques - Trigonometric functions, specifically sine and cosine, are useful for encoding cyclical features due to their periodicity, boundedness, and definition for all real values [4] - Cyclical feature engineering is crucial to capture the cyclic nature of features, otherwise crucial information is lost [3] - Standard linear representation of cyclical features does not fulfill the properties of proximity between recurring values and equal distance [3] Cyclical Features - Datasets have cyclical features with a recurring pattern, exhibiting periodic behavior [1] - Examples of cyclical features include hour of the day, day of the week, and month of the year [2][6] - When representing 'hour of a day' as a cyclical feature, the central angle (2π) denotes 24 hours [4] - For 'day of the week', the central angle (2π) represents 7 days [5] Machine Learning Model Improvement - The model will find it easier to utilize the engineered cyclical features for modeling [5] - Trigonometry can help improve machine learning models by appropriately representing cyclical features [1]