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A Feature Engineering Method in Machine Learning

  • Neelam Tyagi
  • Aug 19, 2021
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What is a feature?


Why do we need the engineering of it?


In particular, all machine learning algorithms employ some sort of input data in order to make outputs where this input data compose features (a usual form of structured columns). Algorithms demand specialized features having the characteristics that work properly, a lot of effort is invested to assure the proper working of algorithms that starts with an unusual shape of available dataset. This is where the requirement of feature engineering rises. 


The engineering of features is the process of catching what available data is appropriate to use for machine learning algorithms, these engineered features are also responsible for testing the accuracy of models and further improving it.


Feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data.-medium


In this blog, we will learn the significance of feature engineering and its role in enhancing data quality for the machine learning process.


(Must learn: Machine learning tutorial)


What is Feature Engineering?


Feature engineering is the process of creating new input features for machine learning.


In easiest words, Feature Engineering is the method of creating new features from existing data for machine learning models. These features are extracted from accumulated raw data followed by transforming into formats suitable for the machine learning process. For this purpose, domain knowledge of data is essential, along with it, programming and mathematical skills are also mandatory to perform feature engineering methods as equitable features makes ML algorithms favourable.


Another definition of feature engineering is a procedure involving an implementation of data’s domain knowledge for generating features making machine learning algorithms workable is called Feature Engineering. Executing feature engineering correctly increases the predictive efficiency of ML algorithms via making most relevant features from raw data and helping in ML process.


(Also read:What is Predictive Analytics?)


The below video emphasises on understanding the process of feature engineering with a simple example over house pricing prediction example. 

In machine learning, feature engineering incorporates four major steps as following;


  1. Feature creation: Generating features indicates determining most useful features (variables) for the predictive modelling, this step demands a ubiquitous human intervention and creativity. In particular, existing features get projected by addition, subtraction, multiplication, and ratio in order to derive new features holding more predictive efficiency to make informed decisions.

  2. Transformation: Involving the manipulation of predictor variables for augmenting model performance, transformation includes

  • Ensuring the feasibility of model in the context of variety of data it can ingest

  • Improving efficiency and accuracy of model and make it easier to understand

  • Excluding computational errors and putting all relevant features into the range of the model.

  1. Feature Extraction: This step involves an automated generation of novel variables via withdrawing them from raw data resulting in automatic conversion of data volume into more manageable dataset for data modeling. A few examples of feature extraction are cluster analysis, text analytics, edge detection algorithms, and principal component analysis.   

  2. Feature Selection: Feature selection based algorithms fundamentally decipher, determine and prioritize different features in order to identify the most irrelevant feature, or redundant features, can be excluded. Also, most important features, useful for the model, would be prioritized. (From)


Steps in Feature Engineering


Performing feature engineering involves following steps for machine learning algorithms such as;


  1. Data Preparation: The preprocessing conducts manipulation and fortification of raw data from various sources into an optimized format enabling them to be facilitated for data modeling. Data preparation incorporates data augmentation, data cleaning, transmission, synthesis, ingestion, and data loading.

  2. Exploratory Analysis: This step results in recognizing and compiling the essential curve in the dataset through data analysis and investigation. For this purpose, data professionalists deploy various data visualization techniques to understand the main characteristics of data, for example;

  • To accurately understand how to manipulate data sources

  • To decide appropriate statistical techniques for data analysis

  • To select the correct data feature for a model. 

  1. Benchmarking: Benchmarking indicates putting a bottomline criteria as an accuracy measurement to which remaining variables get compared with the objective to decrease the rate of error and advancing the predictability of data models. For benchmarking, data experts conduct experimentation, testing and optimization metrics with domain knowledge, tech expertise and business implementation.   


(Suggested blog: Data mining tools)


Importance of Feature Engineering


We have understood the lucrative features could influence the outcomes of predictive models, following are the significance of this process;


  1. Reduce complexity:


Raw data as input are fed to algorithms to build predictive models, engineered features can provide assistance to these algorithms to make instructive predictions. 


Simply, for beginners, accurate engineered features can curtail complexity of models, and improved shape and purpose of the model will make the ML process more proficient. Feature engineering makes the machine learning models easier to understand, construct, manipulate and sustain.


  1. Increase accuracy:


From transforming data variables to suitable format, feature engineering is an extensive process. For example, machine learning models can ingest numerical data format, but some conditions arise where continuous values need to convert into discrete values. 


Such as, a feature has huge upper boundary values that could attract multiple outliers, therefore, it is required to transform data from continuous format to discrete format. 


(Also read: Machine learning methods)



Feature Engineering Techniques


  1. Imputation


Dealing with inadequate data, missing values that are results of data restriction, human interruption, common error, insufficient data sources, etc, feature engineering is helpful in maintaining such flow of data. Missing values influence the performance of algorithms, therefore the technique “imputation” is responsible for handling the anomalous inside data. 


For example, dropping the missing values from complete rows or columns by a large percentage of missing values. But at the same time, in order to conserve the data size, it is advisable to impute the missing data, such that;


  • For numerical data imputation,a default value can be considered to impute in a column, and filling missing values with mean or medians of the columns

  • For categorical data imputation, missing values can be interchanged with maximum occurred value in a column.


  1. Outliers


Outliers are the considerably deviated values or data points observed too far from the rest data points such that they disfigure models’ interferences. In order to handle outliers, the technique initially determines outliers and then cuts them out. Outliers can be recognized with standard deviation. 


  • For example, a value has a definite distance to an average but greater than a certain value, this value can be considered as an outlier. 

  • Z-score can also be deployed to detect outliers, for example, they can be detected using percentiles.


(Related blog: Z-test vs T-test)


  1. Log transform


Considering the condition of skewness in data modelling that signifies the measurement/ determination of how asymmetric a data distribution can be, a skewed dataset can impact the performance of a model. Here, log transform aids to stabilize the skewness of a dataset.


Aimed to shape dataset distribution approximately to be normal upto some extent level, log transform normalized the differences in magnitude in data.


For instance, the differences  between the ages 10 and 20 is different from the difference between the ages 50 and 60, although they are the same in terms of year differences but different in terms of magnitude. 


This technique “Log transform” diminishes the influence of outliers that results in a more robust data model. However, the technique works only with positive values.


  1. Binning


In machine learning models, overfitting is the condition referring to more number of parameters than can be accounted for with a dataset, noisy data invites overfitting. Consequently, one of the techniques “binning” can be used to normalize the noisy data.The process incorporates segmenting different features (either categorical or numerical) into different bins.


Amidst performance and overfitting, the binning has a trade-off such that each occurrence of binning may yield in more regularized data. With regularization, data coefficient shrinks estimated to zero in order to avoid possibilities of overthinking.


(Recommended blog: L2 and L1 regularization)


  1. Feature Split


This technique involves the splitting of features intimately into two or more parts and can be performed to make new features, or to support algorithms for enhanced understanding of the dataset. 


Splitting of features enables the novel features to be clustered and binned that could be resulting in unearthing potent and significant information via this process and hence improving the performance of data models.




“Coming up with features is difficult, time-consuming, and requires expert knowledge. “Applied machine learning is basically feature engineering.” — Prof. Andrew Ng


  • Feature engineering is devising suitable features from existing data to improve the performance of machine learning models by transforming data to generate better outcomes. On the other hand, deploying irrelevant features yields in making more complicated models to achieve the same level of performance.

  • In general, at first stage feature engineering is applied for generating additional features, in second stage, feature selection is performed resulting in eradicating redundant, irrelevant, or highly correlated features. 

  • The method entails the process of creation, transformation, extraction and selection of features (variables) that are essential for making a favorable ML algorithm.

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