👉 Assists computing is a statistical technique used in machine learning, particularly in the context of predicting continuous outcomes, to estimate the contribution of each feature (or input variable) to the prediction. It works by comparing the actual outcome with the predicted value, calculating the difference (or "assist"), and then averaging this difference across all possible combinations of feature values, effectively normalizing the contribution of each feature. This method is especially useful for understanding which features most significantly impact the model's predictions, and it helps in feature selection by identifying and emphasizing those with the largest assists. By focusing on these key contributors, assists computing aids in simplifying models and improving their interpretability.
