Feature Selection and Feature Engineering in Machine Learning
The adoption of machine learning has rapidly transformed multiple industries. It empowers businesses to make informed decisions and gain valuable insights from data. Two key techniques, namely feature selection and feature engineering, play a crucial role in enhancing the performance and accuracy of machine learning models. In this era of exponential data growth, extracting relevant and informative features from vast datasets becomes imperative for optimizing predictive models.
According to a survey conducted by CrowdFlower, 80 Data Scientists dedicated a significant portion of their time, around 60%, to the crucial task of cleaning and organizing data. This finding emphasizes the importance of possessing expertise in engineering and feature selection.
Feature selection plays a crucial role in improving model accuracy, reducing overfitting, and enhancing computational efficiency. By transforming raw data into meaningful representations, feature engineering enables models to effectively capture relevant patterns. Given the current data landscape characterized by its massive volume(approximately 328.77 million terabytes generated on a daily basis) and complexity, these techniques have become increasingly important for effective analysis. This article explores the key concepts of feature selection and engineering in machine learning.
What is Feature Engineering
The process of feature engineering involves carefully selecting and transforming variables or features within your dataset. This is done when creating the predictive model by using machine learning techniques. To effectively train your machine learning algorithms, it is necessary to first extract the features from the raw dataset you have collected. This step allows for data organization and preparation before proceeding with training.
Otherwise, gaining valuable insights from your data could prove challenging. The process of feature engineering serves two primary objectives:
- Providing a compatible input dataset for machine learning algorithms.
- Modelling machine learning to improve performance.
Feature Engineering Techniques
Here are some techniques that are used in feature engineering:
Imputation
Feature engineering involves addressing issues such as inappropriate data, missing values, human errors, general mistakes, and inadequate data sources. The presence of missing values can significantly impact the algorithm’s performance. To handle this issue, a technique called “imputation” is used. Imputation helps in managing irregularities within the dataset.
Handling Outliers
Outliers refer to data points or values that deviate significantly from the rest of the data, negatively impacting the model’s performance. This technique involves identifying and subsequently removing these aberrant values.
The standard deviation can help identify outliers in a dataset. To explain further, each value within the dataset has a specific distance from the average. However, if the value is significantly farther away than a certain threshold, it will be classified as an outlier. Another method to detect outliers is by using the Z-score.
Log transform
The log transform, also known as logarithm transformation, is a widely employed mathematical technique in machine learning. It serves several purposes that contribute to data analysis and modeling. One significant benefit is its ability to address skewed data, resulting in a distribution that closely resembles a normal distribution after transformation. By normalizing magnitude differences, the log transform also helps mitigate the impact of the outliers on datasets, enhancing model robustness.
Binning
Machine learning often faces the challenge of overfitting, which can significantly impair model performance. Overfitting occurs when there are too many parameters and noisy data. This effective technique in feature engineering called “binning” can help normalize the noisy data. It involves categorizing different features into specific bins.
Feature Split
Feature split involves dividing features into multiple parts, thereby creating new features. This technique enhances algorithmic understanding and enables better pattern recognition within the dataset. The feature splitting process enhances the clustering and binning of new features. This leads to the extraction of valuable information and ultimately improves the performance of data models.
What is Feature Selection?
Feature Selection involves reducing the input variables in the model by utilising only relevant data and removing any unnecessary noise from the dataset. It is the automated process of choosing the most relevant features for the machine learning model, tailored to the specific issue that is trying to be resolved. This involves selectively including or excluding important features while keeping them unchanged. By doing so, it effectively eliminates irrelevant noise from your data and reduces the size and scope of the input dataset.
Feature Selection Techniques
Feature selection incorporates various popular techniques, namely filter methods, wrapper methods, and embedded methods.
Filter Methods
Filter methods are used in the preprocessing stage to choose relevant features, regardless of any specific machine learning algorithm. They offer computational efficiency and effectiveness in eliminating duplicate, correlated, and unnecessary features. However, it’s important to note that they may not address multicollinearity. Some commonly employed filter methods include:
- Chi-square test: The Chi-square Test examines the relationship between categorical variables by comparing observed and expected values. This statistical tool is essential for identifying significant associations between attributes within a dataset.
- Fisher’s Score: Each feature is independently selected based on its score using the Fisher criterion. Features with higher Fisher’s scores are considered more relevant.
- Corelation coefficient: The correlation coefficient quantifies the association and direction of the relationship between two continuous variables. In feature selection, Pearson’s Correlation Coefficient is commonly used.
Wrapper Methods
Wrapper methods, also known as greedy algorithms, train the model iteratively using different subsets of features. They determine the model’s performance and add or remove features accordingly. Wrapper methods offer an optimal set of features; however, they require considerable computational resources. Some techniques utilized in wrapper methods include:
- Forward Selection: Forward Selection is a method that begins with an empty set of features and gradually incorporates the one that brings about the greatest improvement in the model’s performance at each iteration.
- Bi-directional Elimination: Bi-directional Elimination combines forward selection and backward elimination techniques simultaneously, allowing for the attainment of a unique solution.
- Recursive Elimination: To achieve the desired number of features, the Recursive Elimination method considers progressively smaller sets and iteratively removes the least important ones. This ensures a more efficient and refined selection process.
Embedded Methods
Embedded methods combine the advantages of filter and wrapper techniques by integrating feature selection directly into the learning algorithm itself. These methods are computationally efficient and consider feature combinations, making them effective in solving complex problems. Some examples of embedded methods include:
- Regularization: Regularization is a technique used to prevent overfitting in machine learning models. It achieves this by adding a penalty to the model’s parameters. Two common types of regularization methods are Lasso (L1 regularization) and Elastic Nets (L1 and L2 regularization). These methods are often employed to select features by shrinking.
- Tree-based Methods: Tree-based methods, such as Random Forest and Gradient Boosting, employ algorithms that assign feature importance scores. These scores indicate the impact of each feature on the target variable.
Conclusion
Feature selection and feature engineering are two crucial techniques in machine learning that significantly enhance the performance and accuracy of models. In the rapidly advancing era of data explosion, extracting pertinent features from extensive datasets is imperative for establishing optimal predictive models. Both methods effectively boost model performance and accuracy within the context of machine learning.
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