What process reduces the number of features in a data set to simplify the model while retaining essential information?

The process that effectively reduces the number of features in a dataset while preserving the essential information is known as feature selection. This technique involves identifying and selecting a subset of the most relevant features from the original set, thereby simplifying the model and improving its efficiency without losing significant insights.

Feature selection can help mitigate issues such as overfitting, where a model becomes too complex and performs poorly on new, unseen data due to its sensitivity to noise. By retaining only the most relevant features, it facilitates easier model interpretation and can lead to better performance in predictive tasks.

In contrast, feature extraction transforms the original features into a new set of features, often reducing dimensionality through techniques like principal component analysis (PCA). Feature engineering involves creating new features based on existing ones to enhance the predictive capability of the model. Feature reduction, while conceptually similar to feature selection, typically refers more broadly to methods aimed at reducing dimensionality but may not focus specifically on selecting the most informative features.

Thus, the correct process for retaining essential information while simplifying the model is feature selection, which precisely addresses the need to curtail the feature space judiciously.