Feature Selection Techniques in Machine Learning

Feature selection is a way to pick the most important features from a group of features by getting rid of the useless, annoying, or redundant ones.Only a few of the factors in the dataset are useful for building the machine learning model. The rest of the features are either unnecessary or don’t help with building the model. It’s possible that adding all of these unnecessary and redundant features to the dataset will hurt the model’s general performance and accuracy. Hence, it is very important to find and pick the best features from the data while getting rid of the useless or less important ones. In machine learning, this is done with feature selection.

Feature selection is a key machine learning concept that affects model performance. Machine learning works on “Garbage In, Garbage Out” thus we should always feed the model the best and most relevant data.

What is Feature Selection?

The most significant model attributes are selected during feature selection. Feature influences or helps a problem. Every machine learning procedure relies on feature engineering, which primarily consists of two procedures: feature extraction and feature selection. The goal of feature selection and extraction procedures may be the same, yet they are quite distinct from one another. The primary distinction between the two is that whereas feature extraction generates additional features, feature selection focuses on choosing a subset of the initial feature collection.Features selected from relevant data reduce model overfitting by reducing the input variable.

Thus, feature selection may be defined as “the process of automatically or manually selecting the subset of most relevant and appropriate features to be used in creating a model.” The process of feature selection involves either adding the dataset’s most significant features or eliminating its unimportant ones without altering the original features.

Need for Feature Selection

Before adopting any technique, it’s vital to understand its requirement and Feature Selection. Machine learning requires a pre-processed, high-quality input dataset for better results. To train and improve our model, we collect tons of data. The dataset typically contains noisy, irrelevant, and useful data. The large volume of data slows model training, while noise and irrelevant input may make the model unreliable. In order to reduce noise and less-important data from the dataset, feature selection techniques are applied.

Selecting the best characteristics improves model performance.To build a model that automatically crushes cars for spare parts, you need data. This dataset includes automobile model, year, owner, and miles. In this dataset, the owner’s name does not affect model performance because it does not decide if the automobile should be crushed, therefore we may eliminate it and select the other features for model creation.

Here are some machine learning feature selection benefits:

• It helps to prevent dimensionality’s curse.
• It aids in the simplicity of the model, allowing researchers to interpret it more simply.
• It lowers training time.
• It reduces overfitting, which improves generalization.

Feature Selection Techniques

Feature selection strategies are primarily divided into two categories:

Supervised Feature Selection technique:Designed for the labelled dataset, supervised feature selection methods take target variable into account.

Unsupervised Feature Selection technique:Feature selection can be applied on unlabeled datasets without targeting the target variable.

Under supervised feature selection, there exist essentially three approaches:

1. Wrapper Methods

Selection of features in wrapper methodology is done under consideration as a search issue, in which several combinations are generated, evaluated, and compared with others.The method is trained iteratively using a subset of features.Features are added or deleted based on model output; this feature set helps the model to be trained once more.

Some techniques of wrapper methods are:

Forward selection -An empty feature set initiates iterative forward selection. After each iteration, it adds features and evaluates performance to see if it’s improving. The procedure is repeated until the model’s performance is not enhanced by the inclusion of a new variable or feature.

Backward elimination -Backward elimination is the opposite of forward selection, yet it is still an iterative process. This method eliminates the least important characteristic after taking into account every feature. This elimination procedure continues until eliminating features doesn’t improve model performance.

Exhaustive Feature Selection-Exhaustive feature selection is one of the most effective feature selection techniques, as it employs a brute-force evaluation of each feature set. It indicates that this approach returns the feature set with the best performance after trying and creating every feasible feature combination.

Recursive Feature Elimination-Recursive feature elimination selects a decreasing subset of features. Each feature set is used to train an estimator, with importance determining significance.

2. Filter Methods

• Features of the Filter Method are chosen using statistical measurements. The features are selected as a pre-processing phase in this method, which is independent of the learning algorithm.
• By employing several metrics through ranking, the filter approach removes duplicated columns and irrelevant features from the model.
• Filtering data has the benefit of requiring less calculation time and avoiding overfitting.

The following are a few typical filter methods:

• Information Gain
• Chi-square Test
• Fisher’s Score
• Missing Value Ratio

Information Gain: Entropy reduction from dataset change depends on information gain. By computing the information gain of each variable in relation to the target variable, it can be applied as a feature selection method.

Chi-square Test:One method for figuring out how the category variables relate to one another is the chi-square test. Pick features having the best chi-square values between them and the target variable.

Fisher’s Score: Fisher’s score is a popular supervised feature selection method. The variable’s rank on the fisher’s criteria is returned in descending order. The variables with a high fisher’s score can then be chosen.

Missing Value Ratio:Using the missing value ratio, compare the feature set to the threshold value. Finding the missing value ratio is the number of missing values in each column divided by the total observations. The variable above the threshold can be dropped.

3. Embedded Methods

Low-cost embedded methods combined filter and wrapper benefits with feature interaction. These quick filter-like processing approaches are more precise.

How to choose a Feature Selection Method?

Machine learning engineers must know which feature selection strategy works best for their model. Choosing a statistical measure for feature selection is easy when we know variable datatypes.First, we must determine input and output variables.

Machine learning has two primary variables:

Numerical Variables:A continuous variable like integer or float.
Categorical Variables:Category variables (Boolean, ordinal, nominal).

Here are some filter-based feature selection univariate statistical measures:

1. Numerical Input, Numerical Output:Predictive regression modeling makes use of numerical input factors. The Correlation coefficient is the usually applied technique for such a situation.
2. Numerical Input, Categorical Output:Classification predictive modeling uses numerical input and categorization output. Again, correlation-based methods with categorical output should be utilized.
3. Categorical Input, Numerical Output:Regression predictive modeling with categorical input. An alternative regression problem. Use the same measures as above, but in opposite order.
4. Categorical Input, Categorical Output:The categorical input factors are used in this case of classification predictive modeling.Chi-Squared Test is the method most often used in this situation. In this case, we can get information.

Conclusion:

Feature selection is a very complicated and extensive topic of machine learning, and many studies have previously been conducted to determine the optimal methods. There is no set guideline for the optimum feature picking approach. However, selecting the method is up to a machine learning engineer who can combine and develop approaches to discover the optimal solution for a certain situation. One should test a range of model fits on distinct subsets of features chosen using different statistical measures.