Your go to Guide for Feature Engineering

Feature engineering is the process of developing new features or changing existing ones to improve the performance of a machine-learning model. It entails extracting useful information from raw data and converting it into a model-friendly format. The goal is to increase model accuracy by giving more useful and relevant data.

 

Brief Introduction

 

Feature engineering is the process of transforming raw data into useful information for machine learning models. In other terms, feature engineering refers to the process of developing predictive model features. A feature, sometimes known as a dimension, is an input variable that generates model predictions.

 

As model performance is heavily reliant on the quality of data used during training, feature engineering is an important preprocessing strategy that entails identifying the most relevant parts of raw training data for both the prediction job and the model type under consideration.

 

All machine learning algorithms provide outputs based on some input data. Input data contains a large number of features that may not be in the right format to provide to the model directly. It requires some processing, which feature engineering can help with. Feature engineering achieves primarily two goals:

 

It prepares an input dataset in the format required by a given model or machine learning method, Feature engineering dramatically improves the performance of machine learning models.

 

Effective feature engineering has a substantial impact on the performance of machine learning models. It enables algorithms to gather important information and accurately generalize to previously unknown data. Understanding the domain and the problem at hand allows data scientists to uncover relevant elements that improve the predictive ability of their models.

 

Also Read | Feature Engineering: Process and Techniques

 

Techniques for Feature Engineering

 

While understanding the training data and the targeted problem is an indispensable part of feature engineering in machine learning, and there are no hard and fast rules as to how it is to be achieved, the following feature engineering techniques for machine learning are a must-know for all data scientists:

 

1. Imputation

 

Imputation is the process of managing missing values in data. While removing entries that do not contain certain values is one solution, it may result in the loss of valuable data. This is where imputation can help. It can be categorized into two sorts.

 

Categorical Imputation: Missing categorical variables are often replaced by the most usually occurring value in other records. Missing numerical values are usually replaced by the average of the equivalent value in other records.

 

2. Discretization

 

Discretization is the process of organizing data values logically into bins (or buckets). Binning can apply to both numerical and categorical data values. This could assist prevent overfitting, but at the expense of data granularity. The data can be grouped as follows: Grouping of Equal Intervals, Grouping by equal frequency (of observations in bin),Grouping based on decision tree sorting (to establish a relationship with target. 

 

3. Categorical Encoding

 

Categorical encoding is a technique for converting categorical information into numerical values that are typically easier for an algorithm to grasp. One hot encoding (OHE) is a popular method of category encoding. Categorical values are turned into plain numerical 1s and 0s without losing any information. As with other approaches, OHE has drawbacks and should be used carefully. It could significantly increase the number of features, resulting in strongly connected features.

 

4. Handling outliers

 

Outliers are extraordinarily high or low values in a dataset that are unlikely to occur in normal circumstances. Because these outliers may have an undesirable effect on your prediction, they must be treated properly. There are several ways for dealing with outliers, including:

 

Records with outliers are eliminated from the distribution. However, the presence of outliers over multiple variables could result in losing out on a large portion of the datasheet with this method. Replacing values: The outliers could alternatively be treated as missing values and replaced by using appropriate imputation.

 

5. Variable transformations

 

Variable transformation strategies are useful for normalizing skewed data. One such widely used transformation is the logarithmic transformation. Logarithmic transformations compress larger numbers while expanding smaller values. This leads to less skewed values, particularly in heavy-tailed distributions. Other variable transformations utilized include square root and box-cox transformations, which are generalizations of the former two.

 

How to increase Feature engineering efficiency?

 

Feature engineering is an essential component of every machine learning application since the developed and selected features have a significant impact on model performance. Features that are relevant to the situation and appropriate for the model will improve model accuracy. Irrelevant features, on the other hand, would lead to a "garbage in, garbage out" problem in data analysis and machine learning.

 

Feature engineering is time-consuming, error-prone, and requires domain understanding. It depends on the problem, the dataset, and the model, hence there is no single solution for solving all feature engineering difficulties. However, there are some ways to automate the feature creation process.

 

Featuretools is an open-source Python library for automating feature engineering. Featuretools generates feature sets for structured datasets using an approach known as deep feature synthesis.

 

There are additional AutoML solutions that enable automated feature engineering. Check out our thorough AutoMl guide for more details. There are MLOps platforms that include automated feature engineering tools. Please refer to our post on MLOPs tools and our data-driven list of MLOps platforms.

 

However, it should be highlighted that automated feature engineering methods rely on algorithms and may be unable to incorporate significant domain knowledge that a data scientist possesses.

 

Also Read | AutoML: Types, Strategies, Pros, and Cons

 

Final Words

 

Feature engineering is an iterative process in data science that selects, creates, and transforms features to improve model performance. It is critical for extracting useful information from raw data, reducing dimensionality, and increasing the predictive potential of machine learning models.

 

Data scientists can gain important insights from their data by using feature engineering techniques such as one-hot encoding, feature scaling, binning, polynomial features, and feature extraction.

 

Data analytics and data visualization are critical components of the feature engineering process. They offer insights into the dataset, help in feature selection, and drive decision-making. Pandas, NumPy, Matplotlib, and Seaborn are all useful tools for efficient data processing and visualization.

Also Read | The Different Types Of Classifiers In Machine Learning