The field of study that deals with the capability of computers to learn and read without explicit programming is called machine learning. There are feature selection techniques in machine learning that help in reducing the noise by taking in only the relevant data after the pre-processing. The techniques have the ability to choose the relevant variables according to the type of user’s problem. In this article, we will be discussing more about the selection techniques, feature selection methods such as wrapper method, filter method, embedded method, etc., along with the feature selection methods and the method for choosing the right feature selection model.
Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. The techniques have the ability to choose the relevant variables according to the type of user’s problem. In case any data comes up that is not relevant to the requirement, it tends to slow down the efficiency process of the model and also decrease the accuracy. Therefore, it is very important to have appropriate feature selection techniques for the models in order to have better outcomes and accuracy.
The main idea of working with selection techniques is to manually extract the relevant settings from the parent set to have high-accuracy model structures.
The techniques are divided into the category of supervised and unsupervised learning. These two categories are further divided into 4 main methods for selecting the features.
There are statistical ways for selecting the features using the filter method. The features are selected in the pre-processing stage as there is no learning process involved in this. The aim of this approach is to filter out the unrequired and irrelevant features by using matrices and ranking methods. The most important advantage of using the filter method is that it does not overfit the data.
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In this method, a user makes different combinations that are evaluated or compared with a lot of other possible combinations. In this way, the feature selection is done. A subset of features is selected and the algorithm is trained based on the subset. The output of the algorithm then decides if the features will be added or not. This method is further based on 4 types which are:
This is a great method for feature selection as it has the advantages for both filter and wrapper methods collectively. The processing time in the embedded method is very high just like the filter method, however, they provide more accurate outcomes.
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There are a few techniques involved with embedded methods which are:
This approach takes in features as small-sized samples. The main idea is to select the features using instance learning. The features that correspond to the instances are selected as they are relevant to the algorithm.
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This model is defined as the class of machine learning methodologies where the user can train with the help of continuous and well-labelled data. For instance, the data can be historical data where the user wishes to predict whether a customer will take a loan or not. Supervised algorithms tend to train over the well-structured data after the preprocessing and feature characterization of this labelled data. It is further tested on a completely new data point for the prediction of a loan defaulter. The most popular supervised learning algorithms are the k-nearest neighbour algorithm, linear regression algorithm, logistic regression, decision tree, etc.
This is further divided into 2 categories:
This model is defined as a class of machine learning methodologies where the tasks are performed using the unlabelled data. Clustering is the most popular use case for unsupervised algorithms. It is defined as the process of grouping similar data points together without manual intervention. The most popular unsupervised learning algorithms are k-means, k-medoids, etc.
This is further divided into 2 categories:
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It is very important for machine learning engineers as well as researchers to understand which feature selection model is most suitable for them. The most data types are known by the engineer, the easier it will be for him to choose properly and wisely. This whole concept is based on 4 main approaches which are:
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Conclusion:
The process of selecting features in machine learning is a vast concept and it involves a lot of research to select the best features. However there is no hard and fast rule for making the selection, it all depends on the type of model and its algorithm and how a machine learning engineer wants to pursue it. Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing.
In this article, we have talked about various feature selection methods that use certain algorithms for making the best possible outcomes and why we should make this feature selection method. Along with this, we have talked about how we can finalise the best feature selection model to work with.
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As we take a lot of training data involving a number of features, these selection techniques help to reduce the variables for making the best feature set possible.
Fisher’s Score is the most popular algorithm used for the feature selection process.
We often use filtering in the pre-processing stage and the steps involving the selection of features are not dependent on the algorithms. The selection of features is done depending on their results scores after performing various tests such as statistical tests on them.