Understanding Feature Engineering

Feature engineering stands out as one of the fundamentals of machine learning.

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In our article, we will look over why feature engineering is essential and the main tools used in this process.

What is feature engineering in simple words?

Feature engineering converts raw data into features that apply to machine learning and statistical learning. Actions with data in feature engineering involve transformation, selection, manipulation, and so on. Examples of features that result from feature engineering are various objects, sounds, colors, etc. All these objects are variables that can be measured and used for learning purposes.
Feature engineering is highly important for machine learning as it is the stage that touches the process of creating algorithms by designing features. Models of data must be useful and accurate, so applying feature engineering helps in the work of data scientists.

Feature engineering techniques

We will look over not all possible feature engineering techniques for machine learning but only some of the best ones.

Log transform

One of the most popular techniques that are mainly used by data scientists is for transforming distributions. Thanks to the log transform technique, specialists can handle data adjusting them to applications.

Outlier handling

Outliers can be easily removed from datasets to make the presentation more correct. Note that this technique cannot be applied at any time and must be provided before the model training. The methods of outlier handling are replacing values, removal, discretization, and capping.

Scaling

This technique is among the most complicated ones, and at the same time, many data scientists consider that scaling is very important for machine learning. Scaling relates to training predictive models by scaling sets of features, making them similar by range. Scaling is mainly provided in two ways:

Standardization

The method of scaling is also called a z-score normalization. Standard feature deviation influences the range of features that reduce the effect of outliers.

Normalization

This method assumes that values are scaled independently without any influence on the feature’s distribution.

Feature engineering tools

Specialists in machine learning use many various feature engineering tools that help transform data into needed features. The main function of these tools is to speed up and automate the process. Thanks to practical feature engineering tools, one can generate many features in a short period. Here are several examples of the disc used tools:

TSFresh

This tool is a part of a Python coding package. Thanks to TSFresh, one can easily calculate many features for both regression and classification tasks, extract various things, etc. TSFresh can be integrated with other feature tools. Many specialists in data science consider TSFresh one of the best Python tools for feature engineering.

FeatureTools

The tool that allows automation of the process of feature engineering by integration with other tools in the machine learning system. FeatureTools is popular thanks to its native and easy starting pack and good documentation. FeatureTools uses a low-level function library that helps in generating features.

OneBM

This tool is used to interact with tables of databases. OneBM can recognize all types of data, both relational and non-relational, including numerical, categorical, sets of numbers, time series, categories, texts, and other ones. OneBM allows generating simple features and complicated features as well.

AutoFeat

The tool allows the selection of the data units of the variables and eases the process of performing prediction models. AutoFeat is very useful when dealing with logistical data.

Afterall

Now you know that feature engineering is one of the essential processes that lets converting raw data into applicable features for machine learning. The features could be combined into algorithms, measured, transformed, combined, stored, etc. Different techniques and methods are used in feature engineering depending on the type of data and the goals.

We believe that after reading our article, your understanding of feature engineering has become better. We wish you good luck with studying computer science!

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