What technique is commonly applied to improve the accuracy of predictive models?

Feature engineering is a crucial technique used to enhance the accuracy of predictive models. It involves creating new features or modifying existing ones to provide better input for machine learning algorithms. By transforming raw data into a format that is more suitable for modeling, feature engineering helps to highlight the underlying patterns within the data that the model can learn from.

For example, in a dataset where you are trying to predict house prices, you might engineer features such as the size of the house in square feet, the number of bathrooms, or even combine features like the number of rooms per square foot. These engineered features can reveal important relationships that may not be apparent with the original data.

Additionally, well-designed features can lead to improved model performance because they help in reducing noise and enhancing the signal that the model learns from. As the model is exposed to more relevant and informative features, it can make better predictions, thus increasing accuracy.

While techniques like data augmentation, dimensionality reduction, and data imputation also play significant roles in data preparation and model performance, they serve different purposes. Data augmentation expands the training dataset with modified versions of existing data points, which is particularly useful in scenarios with limited data. Dimensionality reduction focuses on reducing the number of input variables, which can simplify models