The correct answer is B. Yes.
Feature selection is a process of selecting a subset of features from a dataset 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 characteristics, such as their correlation with the target variable or their variance. Wrapper methods select features by iteratively building and evaluating a model with different subsets of features. Embedded methods select features by jointly optimizing the model and the feature selection process.
Feature selection can be used to improve the performance of machine learning models by reducing the dimensionality of the data and by removing irrelevant features. This can lead to faster and more accurate models.
In conclusion, the number of features decreases while using feature selection on the data. This is because feature selection removes irrelevant features from the dataset, which reduces the dimensionality of the data. This can lead to faster and more accurate machine learning models.
Here are some additional details about each option:
- Option A: No. This option is incorrect because the number of features does decrease while using feature selection on the data.
- Option B: Yes. This option is correct because the number of features does decrease while using feature selection on the data.