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3 Important Types of Feature Selection in Machine Learning and how to choose them

3 Important Types of Feature Selection in Machine Learning and how to choose them

Machine learning is a subset of artificial intelligence which allows computers to learn without human intervention. A set  of data is provided as input to the model which uses an algorithm to make predictions or make decisions. These input variables are called features. Feature selection is a way of reducing the dimensionality of the feature space by removing the less important features and keeping only the most important ones, which helps in building an optimized model. In this blog post, we will discuss various types of feature selection in machine learning. Applications such as remote sensing, image retrieval etc., use the technique of feature selection.

Feature selection is a way of reducing the dimensionality of the feature space by removing the less important features and keeping only the most important ones. Feature selection has many benefits for machine learning, such as:

  1. It improves the model accuracy by eliminating the noise and bias in the data.
  2. It reduces the model complexity and makes it easier to interpret and explain.
  3. It speeds up the model training and testing time by reducing the computational cost.
  4. It prevents overfitting and improves generalization by reducing the variance in the data.

Feature selection techniques

There are different types of feature selection in machine learning, which can be broadly classified into three categories: filter methods, wrapper methods, and embedded methods.

Feature Selection Techniques in Machine Learning
Feature Selection Techniques in Machine Learning

Filter Methods

Filter method in machine learning is based on statistical measures or tests that evaluate the relevance of each feature independently of any machine learning algorithm. Filter methods are fast and simple to apply, but they do not consider the interactions among features or the impact of feature selection on the model performance. Some common filter methods are:

Variance threshold

This method removes the features that have low variance, which means they have similar values for most of the observations. Low variance feature selection and extraction in machine learning do not contribute much to the model prediction and may introduce noise or bias. The variance threshold can be set manually or based on some criterion.

Correlation coefficient

This method measures the linear relationship between two features or between a feature and the target variable. A high correlation coefficient indicates a strong dependency between two features or a strong influence of types of feature selection in machine learning on the target variable. Correlation coefficient can be used to remove highly correlated features or select highly correlated features with the target variable.

Chi-square test

This method tests the independence between two categorical features or between a categorical feature and a categorical target variable. A low chi-square value indicates a high dependence between two features or a high influence of a feature on the target variable. Chi-square test can be used to remove highly dependent features or select highly influential features with the target variable.

Information gain

This method measures the reduction in entropy (or uncertainty) of the target variable after splitting the data based on a feature. A high information gain indicates a high relevance of a feature for predicting the target variable. Information gain can be used to select the most informative features with respect to the target variable.

Wrapper Methods

Wrapper methods are based on evaluating the subset of features using a specific machine learning algorithm and a performance metric. Wrapper methods are more computationally expensive than filter methods, but they consider the interactions among features and the impact of variable selection methods machine learning on the model performance. Some common wrapper methods are:

  1. Forward selection: This method starts with an empty set of features and iteratively adds one feature at a time that maximizes the model performance until no further improvement is possible or some stopping criterion is met.
  2. Backward elimination: This method starts with a full set of features and iteratively removes one feature at a time that minimizes the model performance until no further improvement is possible or some stopping criterion is met.
  3. Recursive feature elimination: This method recursively eliminates one or more features based on their importance scores assigned by a machine learning algorithm (such as linear regression or decision tree) until some desired number of features is reached or some stopping criterion is met.

Embedded Methods

Embedded methods are based on incorporating feature selection as part of the machine learning algorithm itself. Embedded methods are more efficient types of feature selection in machine learning than wrapper methods, as they do not require repeated evaluation of different subsets of features. Embedded methods also consider the interactions among features and the impact of feature selection on the model performance. Some common embedded methods are:

  1. Lasso Regression: This method applies a regularization technique called L1-norm that penalizes the model for having large coefficients for each feature. As a result, some of the coefficients become zero, which means those features are eliminated from the model.
  2. Ridge Regression: This method applies a regularization technique called L2-norm that penalizes the model for having large coefficients for each feature. As a result, some of the coefficients become very small, which means those features have less influence on the model.
  3. Elastic Net Regression: This method combines L1-norm and L2-norm regularization techniques to balance between feature elimination and feature shrinkage.
  4. Decision Tree: This method splits the data based on the most informative features at each node until some stopping criterion is met. The importance of each feature can be measured by the reduction in impurity (or increase in information gain) after each split.

Use of Python for feature selection

A large number of machine learning applications use Python, for its simplicity and ease of use. It has many libraries and tools that can help with different types of feature selection in machine learning python.

Why we use python for feature selection in machine learning

Feature selection and extraction in machine learning can improve the performance, accuracy and interpretability of your machine learning model. It can also reduce the complexity, overfitting and training time of your model.

There are many techniques of feature selection in machine learning python, but here are three major ones:

Univariate Selection

This technique uses statistical tests to measure the relationship between each feature and the target variable. You can use the SelectKBest class from sklearn.feature selection in machine learning python module to select a specific number of features based on different tests, such as ANOVA F-value, chi-square or mutual information.

Feature Importance

This technique assigns a score to each feature based on how important it is for the model. You can use the feature, importance, and attribute of some models, such as decision trees or random forests, to get the scores of each feature. You can also use the SelectFromModel class from sklearn.feature selection module to select features based on a threshold or a predefined number of features.

Correlation Matrix

This technique measures the correlation between each pair of feature selection and extraction in machine learning and between each feature and the target variable. You can use the corr() method of pandas DataFrame to get the correlation matrix and visualize it using a heatmap. You can then remove features that are highly correlated with each other or have low correlation with the target variable. 

Conclusion

Feature selection and extraction in machine learning is a crucial step in machine learning that reduces the dimensionality, complexity and noise of the data. It improves the accuracy, speed and comprehensibility of the model by selecting the most relevant features. Various  techniques such as filter, wrapper and embedded methods can be used for feature selection. if you want to learn about the skills required for machine learning click now.

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