Feature selection in machine learning impacts model performance and efficiency. In this, you may encounter the challenge of dealing with large datasets containing various features, where not all variables contribute equally to predicting the target variable. Adding redundant variables decreases the model’s generation capability and can also reduce the overall accuracy of a classifier. Additionally, adding more variables to a model improves the overall complexity of the model.
Feature selection is part of the feature engineering process, in which data scientists prepare data and curate a feature set for machine learning algorithms.
What is feature selection in machine learning?
Feature selection in machine learning is the process of selecting the most relevant features of a dataset to use when building and training a machine learning model. By removing the feature space to a chosen subset, feature selection develops AI model performance while lowering its demand.
- Improved Model Performance: This directly reduces the risk of overfitting, making the model robust when applied to new, unseen data.
- Reduced Training Time: Removing redundant features decreases overfitting and makes a model better able to generalize the new data.
- Model Interpretability: It refers to the ability to understand and explain how a machine learning model makes its predictions or decisions.
- Faster Training and Reasoning: Working with a reduced set of features speeds up both the training and reasoning stage of the machine learning pipeline.
Feature selection techniques in machine learning
The feature selection techniques in machine learning aim to identify the best set of features, enabling the construction of optimised models for studying examined occurrences. The feature selection in machine learning can be broadly classified into the following categories.
- Supervised Techniques
- Unsupervised Techniques
- Supervised Techniques: These techniques are used for labeled data to recognize the relevant features for improving the regulation models like classification and regression. For example: linear regression, decision tree, SVM, etc.
Feature selection method in machine learning
Let’s explore three main categories of feature selection methods in machine learning:
1. Filter Methods: The filter method in ML assesses the connection of features based on statistical measures on correlation with the target variable.
- Information Gain: Information gain measures the reduction in entropy values by splitting a dataset according to a given feature.
- Chi-Squared Test: It compares the observed values from different attributes of the dataset to its expected value.
- Fisher’s Score: Fisher’s score is a way to find out which things are most important in the group. And it’s particularly useful for continuous features in classification problems.
- Correlation Coefficient: This is a measure of the linear relationship between 2 or more variables. Through correlation, you can predict one variable from the other.
- Variance Threshold: From this approach all features will be removed whose variance doesn’t meet the specific threshold. By default, it removes all zero-variance features, i.e., features with the same value in all samples.
2. Wrapper Methods: This method uses a specific ML algorithm to estimate feature importance.
- Forward selection: It starts with an empty feature set and iteratively adds features that improve model performance the most.
- Backward Selection: This works exactly opposite to the forward feature selection and iteratively removes the least significant ones.
- Exhaustive Feature Selection: This tries every possible combination of the variables and returns the best-performing subset.
- Recursive Feature Elimination: It trains a model, ranks features based on importance and eliminates them one by one until the desired number of features is reached.
3. Embedded Method: The feature selection in embedded method incorporates feature selection into the model training process.
- LASSO Regularization (L1): Regularization techniques consist of adding a penalty to the different parameters of the machine learning model to decrease the freedom of the model to avoid overfitting.
- Random Forest Importance: These algorithms provide feature selection by selecting the most important features for splitting nodes based on criteria like impurity or information gain.
- Unsupervised Techniques : These techniques are used for unlabeled data and allowed to discover patterns and insights without any explicit guidance or instruction. Further, the supervised techniques can be divided into various methods, known as:
- Principal Component Analysis (PCA): It is an effective technique that helps us to find the most important parts of the dataset. Also, works by finding the principal components that capture the maximum variance in the data.
- Independent Component Analysis (ICA): ICA is a feature selection technique that helps us to understand how different things combine together. It’s mainly useful when you want to identify the sources of a signal rather than just the principal components.
- Non-negative Matrix Factorization (NMF): NMF is a technique that decomposes a non-negative matrix into two non-negative matrices. It’s mainly used for text mining and image processing tasks.
Feature subset selection in machine learning
The feature subset selection in machine learning is important to supervise ML not just because it results in better models but also because of the understanding that it offers.
Feature subsets are estimated using correlation-based strategies in a new feature selection algorithm. The three common machine learning algorithms are given to evaluate the algorithm’s effectiveness, and experiments on datasets show that algorithm improves significantly.
Why is feature selection Important?
Knowing which features to focus more on is the essential component of feature selection. Some features are highly desirable for modelling, while others can lead to unsatisfactory results. In addition to how they affect variables, feature importance is determined by:
1. Ease of modelling: If a feature is easy to model, the overall machine-learning process is simpler and faster, with fewer opportunities for error.
2. Easy to regularize: Features that take well to regularization will be more efficient to work with.
3. Disentangling Causality: Disentangling causal factors from an observable feature means identifying the underlying factors that influence it.
How to do feature selection in machine learning?
The feature selection in machine learning is an important aspect that involves choosing the most relevant features from the dataset. If the issue is unsupervised, it can be resorted to by using statistical tools like correlation, chi-square test, etc. On the other hand, if the problem type falls under supervised method, the filter, intrinsic, or wrapper methods can be used.
Conclusion
The feature selection in machine learning has a significant impact on model performance and capability. The methods in feature selection helps to identify the most relevant attributes, leading to improved accuracy, and faster training times.
Both supervised and unsupervised techniques offer valuable tools to increase machine learning models, from filter-based methods like information gain to wrapper techniques.
Whether you’re working with large datasets or seeking to increase the precision of the models, choosing the right feature selection technique is important. Ready to transform your skill set? Explore Ze learning Labb’s Data Science and GEN AI courses to start your journey.