The correct answer is C. Feature selection.
Feature selection is the process of selecting a subset of features from the original set of features that are most relevant to the target variable. This can be done using a variety of methods, such as filter methods, wrapper methods, and embedded methods.
Filter methods select features based on their individual importance, without considering the relationship between the features. Wrapper methods select features by evaluating the performance of a model on a holdout set, while varying the set of features that are used. Embedded methods select features by jointly optimizing the model’s performance and the feature selection process.
Feature selection is an important step in machine learning, as it can improve the performance of the model by reducing the dimensionality of the data and by removing irrelevant features.
A. Data preprocessing is the process of cleaning and transforming the data so that it can be used by a machine learning model. This can involve removing duplicate data, filling in missing values, and normalizing the data.
B. Model evaluation is the process of assessing the performance of a machine learning model. This can be done by comparing the model’s predictions to the actual values, or by using a metric such as accuracy or precision.
D. Hyperparameter tuning is the process of finding the optimal values for the hyperparameters of a machine learning model. Hyperparameters are the parameters that control the learning process, such as the number of iterations or the learning rate.