Feature Engineering: Process and Techniques

The act of choosing, modifying, and converting unprocessed data into features that can be applied in supervised learning is known as feature engineering. It could be important to create and train better features in order to make machine learning effective on new tasks. 

 

A "feature," as you may know, is any quantifiable input that may be used in a predictive model; examples include the color of an object's surface or the sound of a person's voice. Simply put, feature engineering is the process of employing statistical or machine learning techniques to transform unprocessed observations into desired features.

 

What is Feature Engineering?

 

In general, input data is used by all machine learning algorithms to produce output. The input data continues to be presented in a tabular format with rows denoting instances or observations and columns denoting variables or attributes; these attributes are frequently referred to as features. 

 

In computer vision, an image is an example, although a feature might also be a line in the image. Similar to this, a document can be an observation in NLP, and the characteristic could be the number of words. Therefore, we can define a feature as a characteristic that has an impact on or serves a purpose for an issue.

 

Machine learning's preprocessing step, feature engineering, extracts features from unprocessed data. It aids in better communicating a fundamental issue to predictive models, increasing the model's accuracy for unobserved data. The feature engineering method chooses the most practical predictor variables for the model, which is composed of predictor variables and an outcome variable.

 

Since 2016, some machine learning programmes that aid in automatically extracting features from raw data have also adopted automated feature engineering. Four operations make up the majority of feature engineering in machine learning: feature creation, transformations, feature extraction, and feature selection.

 

We’ve explained the process of Feature Engineering below : 

 

1. Feature Creation

 

Finding the most beneficial variables to include in a predictive model is known as feature creation. The procedure necessitates human ingenuity and intervention and is subjective. The addition, subtraction, and ration operations used to construct the new features provide them a tremendous deal of versatility.

 

2. Transformations

 

The feature engineering transformation stage entails modifying the predictor variable to raise the model's precision and effectiveness. By ensuring that all the variables are on the same scale and that the model is flexible enough to accept input from a range of data, for instance, it makes the model simpler to comprehend. 

 

In order to prevent any computational error, it increases the model's correctness and makes sure that all of the features are within the permitted range.

 

3. Feature Extraction

 

A feature engineering procedure that automatically creates new variables by removing existing ones from raw data is called feature extraction. This step's major goal is to decrease the amount of data so that it can be used and managed for data modeling more simply. 

 

Cluster analysis, text analytics, edge detection algorithms, and principal components analysis are examples of feature extraction techniques (PCA).

 

4. Feature Selection

 

Only a small subset of the dataset's variables can be used to build a machine learning model; the remainder are either redundant or useless. The overall performance and accuracy of the model may suffer if all this redundant and pointless information is included in the dataset. 

 

In order to remove the unnecessary or less significant features from the data, it is crucial to discover and choose the most appropriate features from the data, which is accomplished with the aid of feature selection in machine learning. 

 

By eliminating the duplicate, irrelevant, or noisy characteristics from the original feature set, feature selection is a technique for choosing the subset of the most pertinent features.

 

The advantages of feature selection in machine learning are listed below:

 

• It aids in avoiding the dimensionality curse.
• It aids in the model's simplicity so that researchers may quickly analyze it.
• It cuts down on training time.
• It improves generalization by reducing overfitting.

 

Procedure of Feature Engineering

 

Data scientists may have different approaches to feature engineering, however the following are the steps in feature engineering :

 

1. Data Preparation

 

In this preprocessing step, raw data from various sources are manipulated and combined into a common format so that they may be used in a model. Data augmentation, cleansing, delivery, fusion, ingestion, and/or loading are all examples of data preparation.

 

2. Exploratory Analysis

 

Through data analysis and research, this process is utilized to pinpoint and enumerate the key features in a data set. Data visualization is a tool that data science professionals use to better comprehend how to modify data sources, choose the best statistical techniques for data analysis, and select the best characteristics for a model.

 

3. Benchmark

 

Setting a baseline standard of accuracy to which all variables are compared is known as benchmarking. This is done to lower the error rate and increase the predictability of a model. Data scientists with domain expertise and business users experiment, test, and optimize metrics for benchmarking.

 

Techniques for Feature Engineering

 

The main techniques for feature engineering include:

 

1. Imputation

 

Missing values in data sets are a common issue in machine learning and have an impact on how algorithms work. Imputation creates a complete data set that may be used to train machine learning models by substituting missing data with statistical estimates of the missing values.

 

2. One-hot encoding

 

A technique for transforming categorical data into a format that the machine learning algorithm can comprehend and use to improve predictions.

 

3. The bag of words counting algorithm

 

It determines how frequently a word appears in a document. For purposes like document classification and search, it can be used to identify similarities and differences in documents.

 

4. Automated Feature Engineering

 

Using a framework that can be used to solve any problem, this technique extracts relevant and useful features. Data scientists can be more productive by spending more time on other machine learning components thanks to automated feature engineering. Using a framework-based methodology, this strategy also enables citizen data scientists to perform feature engineering.

 

5. Binning

 

Binding, or grouping data, is essential for getting numerical data ready for machine learning. Using this method, a column of numbers can be replaced with categorical values that indicate particular ranges.

 

6. N-grams

 

It is useful for predicting the following item in a series. The n-gram model aids in the study of the text's or document's sentiment in sentiment analysis.

 

7. Feature crossings

 

These are a means to create one feature from two or more categorical features. When certain characteristics work better together than they do separately to identify a quality, this strategy is especially helpful.

 

There are some open source Python libraries that support feature engineering techniques, such as the Feature Tools package, which uses deep feature synthesis, an algorithm, to automatically produce features for relational data sets, to create features from a set of related tables.

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Feature Engineering Process

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Importance of Feature Engineering

 

• For machine learning models, feature engineering is essential since the features in your data have a direct impact on the outcomes you can get with a predictive model. Additionally, while having greater features can help you get better results, this is not the complete picture.

 

• Machine learning models are intricate and rely on a variety of interacting variables. The framing of the problem, the model itself, the quality and quantity of available data, and the chosen and prepared characteristics all play a role in the potential outcomes you might obtain with them. The best features accurately describe the data's underlying structures.

 

• You can select more ideal features thanks to feature engineering. Flexibility is thus achieved as a result. If a model can identify strong data structure, even a model that is not optimal can produce useful findings. Better features offer greater flexibility and make it possible to utilize simpler, quicker, and easier to maintain models.

 

• Simpler models that are not optimal—running the "wrong" parameters, in other words—can nonetheless give useful results because of well-engineered features.

 

Also Read | Self Supervised Learning - Types, Examples and Applications

 

Why is Feature Engineering so challenging?

 

Deep technical expertise and thorough understanding of how each machine learning algorithm functions are needed. Model variety is necessary for effective artificial intelligence (AI), hence it is crucial to train several algorithms on the data, each of which may necessitate a distinct feature engineering approach.

 

It frequently necessitates programming, database, and coding expertise. Testing the effects of newly developed features requires tedious, trial-and-error effort and occasionally yields frustrating insights, such as that the accuracy declined rather than increased as more features were added.

 

It's also essential to have domain knowledge, or an understanding of how the data and the sector interact. For instance, when a product has many names, it's critical to identify which goods are actually identical and ought to be grouped together so that the algorithm can treat them equally.

 

Ideally, try to set aside processing capacity for finding data patterns that haven't been seen before. Apply domain knowledge to the algorithm to "teach" it everything the human team is already aware of. However, it takes expertise and experience to be able to accomplish this and even to perceive what is "known" on the human side.

 

All of these factors make feature engineering a time- and resource-intensive process. The technical skills and domain knowledge required for best-practice feature engineering might take years to acquire. Due to the nature of the learning-by-doing method, using both skill sets within the context of a big data science project can take a few people who possess them years.

 

Also Read | Different Types of Learning in Machine Learning

 

 

Conclusion

 

Despite being in its infancy, feature engineering has great potential to help data scientists prepare data quickly and easily.

 

In data science, feature engineering is a crucial step to maximizing the value of the data at hand. Different feature engineering techniques strive to produce a coherent set of data that is understandable and simple to handle so that machine learning algorithms can produce accurate and trustworthy results.

 

Machine learning algorithms' output quality is influenced by features, and feature engineering seeks to make the features used to train the algorithms better. 

 

Although feature engineering aids in improving the model's performance and accuracy, other techniques can also improve prediction accuracy. There are many more feature engineering techniques besides those stated above, but we have just mentioned the ones that are most frequently applied.