Ans :
Feature selection: During this approach, a subset of the complete set of variables is
selected; as a result, the number of conditions that can be utilised to illustrate the issue is
narrowed down. It’s normally done in one of three ways:
o Filter method
o Wrapper method
o Embedded method
Feature extraction: It takes data from a space with many dimensions and transforms it
into another environment with fewer dimensions.
- Feature selection: It is the process of selecting some attributes from a given collection of prospective features, and then discarding the rest of the attributes that were considered. The use of feature selection can be done for one of two reasons: either to get a limited number of characteristics in order to prevent overfitting or to avoid having features that are redundant or irrelevant. For data scientists, the ability to pick features is a vital asset. It is essential to the success of the machine learning algorithm that you have a solid understanding of how to choose the most relevant features to analyze. Features that are irrelevant, redundant, or noisy can contaminate an algorithm, which can have a detrimental impact on the learning performance, accuracy, and computing cost. The importance of feature selection is only going to increase as the size and complexity of the typical dataset continues to balloon at an exponential rate.
2. Feature Selection Methods: Feature selection methods can be divided into two categories: supervised, which are appropriate for use with labelled data, and unsupervised, which are appropriate for use with unlabeled data. Filter methods, wrapper methods, embedding methods, and hybrid methods are the four categories that unsupervised approaches fall under.
● Filter methods: Filter methods choose features based on statistics instead of how well they perform in feature selection cross-validation. Using a chosen metric, irrelevant attributes are found and recursive feature selection is done. Filter methods can be either univariate, in which an ordered ranking list of features is made to help choose the final subset of features, or multivariate, in which the relevance of all the features as a whole is evaluated to find features that are redundant or not important.
● Wrapper methods: Wrapper feature selection methods look at the choice of a set of features as a search problem. Their quality is judged by preparing, evaluating, and comparing a set of features to other sets of features. This method makes it easier to find possible interactions between variables. Wrapper methods focus on subsets of features that will help improve the quality of the results from the clustering algorithm used for the selection. Popular examples are Boruta feature selection and Forward feature selection.
● Embedded methods: Embedded feature selection approaches incorporate the feature selection machine learning algorithm as an integral component of the learning process. This allows for simultaneous classification and feature selection to take place within the method. Careful consideration is given to the extraction of the characteristics that will make the greatest contribution to each iteration of the process of training the model. A few examples of common embedded approaches are the LASSO feature selection algorithm, the random forest feature selection algorithm, and the decision tree feature selection algorithm.
