# Feature Extraction in Machine Learning: An Easy Guide In 3 Points

Ajay Ohri
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## Introduction

With more and more data being generated daily, one has to differentiate between interesting features extraction and actionable data feature selection. Machine learning and feature extraction in machine learning help with the algorithm learning to do features extraction and feature selection which defines the difference in terms of features between the data kinds mentioned above. Further, in all actionable data, one has to find the features that are relevant and focus on these to resolve the problem in a feature extraction example.

This means intelligent learning in algorithms needs to be discriminant in nature and know the difference between feature selection and feature extraction. Thus the need to know the methods of feature selection and an understanding of the feature extraction techniques are critical to finding the features that most impact decisions and resolve issues.

## 1. What is Feature selection (or Variable Selection)?

Feature selection and feature extraction techniques are what all humans can do. However, for learning algorithms, it is a problem of feature extraction in machine learning and selecting some subset of input variables on which it will focus while ignoring all other input variables. In other words, it affects the Dimensionality Reduction of feature extraction algorithms.

Speaking mathematically, when there is a feature set F = { f1,…, fi,…, fn } the problem in Feature Selection is to find a subset that classifies patterns while maximizing the learner algorithm’s performance abilities. Hence, the list of feature extraction algorithms’ scoring function is denoted by F’, the subset to be found. Note that the algorithm for future selection also maps feature extraction in machine learning to the input variables subset when performing mapping functions.

Hence, the optimal Feature Subset is defined by the classifier’s performance and approximated or estimated to be the Bayes error rate of feature selection algorithms theoretically.

## 2. Feature Selection: The Two Schools of Thoughts

Feature selection has 2 ways of thinking. The coexisting 2 thought schools of feature extraction in machine learning are important from selecting features. When feature extraction methods deal with multi-variant features, the algorithm has to affect Dimensionality Reduction and then move to feature selection as this impacts the learning rate and performance of the algorithm.

• The Curse of Dimensionality:

Since the feature extraction in machine learning training examples number is fixed, for the required accuracy specified, the number of samples and multivariate variables required is seen to grow exponentially, and the performance of the classifier gets degraded with such large numbers of features. The algorithm thus stops learning or slows down. This curse is resolved by making up for the loss of information in discarded variables achieved through lower-dimensional space accurate sampling/ mapping.

• Feature Selection — Optimality:

For optimality in feature extraction in machine learning, the feature search is about finding the scoring feature’s maximising feature or optimal feature. Since in real-life applications, one cannot find the optimal feature. It is computationally a very arduous process searching for feature subsets in the entire space. One uses the optimal subset approximations instead and focuses on finding search-heuristics that are efficient.

## 3. The relevance of Features

The term relevance in feature extraction in machine learning has several definitions. A single variable’s relevance would mean if the feature impacts the fixed, while the relevance of a particular variable given the others would mean how that variable alone behaves, assuming all other variables were fixed. Thus there are 2 degrees of when relevance is weak and when relevance is strong, if and only if the feature is relevant. Redundancy is the term used for irrelevant degrees of relevance.

Weak Relevance: Let’s take a feature fi and the set of all features where Si = {f1, …, fi-1, fi 1, …fn} except for the selected feature. Let’s assign values to all features of Si and denote the new set as si. fi  the selected feature is said to be weakly relevant, if and only if , a subset of features Si‘ subset of Si exists where y, si, xi, and p(Si’ = si’, fi = xi,) > 0, and such that fi is not strongly relevant in p(Y = y | fi = xi; Si’ = si’) ≠ p(Y = y | Si’ = si’. What this means is that there is a subset of features Si’, where the optimal Bayes classifier performance on Si’ is worse than Si’U { fi }.

Strong Relevance: fi the selected feature is strongly relevant, if and only if , there exists some y, si, xi, and p(Si = si, fi = xi,) > 0 such that p(Y = y | fi = xi; Si = si) ≠ p(Y = y | Si = si) meaning the deterioration performance of the optimal Bayes classifier occurs with the removal of fi alone.

## Conclusion

In conclusion, we can see that feature extraction in machine learning, and feature selection increases the accuracy and reduces the computational time taken by the learning algorithm. However, the process of feature extraction in machine learning is complicated and very popularly used for its optimality feature in problems of working with features and spaces with high-dimensionality.

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