Feature Engineering for Machine Learning- Explained with Example

Introduction

The details of your data impact the predictive models you train and the results you can get from them. Even if your analysis data is less than ideal, you can still get the results you desire with a strong set of features. Thus, the concept of feature engineering for machine learning that supports analysis is becoming popular. 

The goal of feature engineering in machine learning is to make models perform better. So, by choosing the most important pieces of information, we give more accurate model predictions. Gain in-depth information about feature engineering for machine learning in this blog.

What is Feature Engineering in Simple Words?

Feature engineering is a machine learning technique that turns accessible datasets into collections of graphics for a particular activity. Hence, this process involves:
 

• Performing data analysis and removing abnormalities, incomplete data, or inaccurate data.
• Reducing duplicates, comparing records, and occasionally, normalising data.
• Removing parameters that have no impact on model behaviour.

Both supervised and unsupervised learning can benefit from this method. Instead of expanding the dataset, with relevant data, we may improve the model's accuracy and response time with fewer variables.

Why is Feature Engineering for Machine Learning Important?

Machine learning's preprocessing step, feature engineering, extracts features from raw information. Feature engineering assists in the following ways:
 

• It aids in better communicating an underlying issue to predictive models, increasing the model's accuracy for unreliable information. 
• The feature engineering method chooses the most practical predictor variables for the model. Thus, it comprises predictor variables and an outcome variable.

Major Steps In Feature Engineering for Machine Learning

Below are the major ML feature engineering steps:

1. Data Preparation

The initial stage is to transform the raw data collected from various sources into a format that the ML model can use. For this, you perform procedures like data fusion, ingestion, loading, and purification. You can now start engineering features.

2. Analysis of Data

To examine the nature of your data, you must run descriptive statistics on datasets and produce visualisations. Further, scan the dataset's columns for corresponding variables and details about them. You can also clean them if necessary.

3. Improving Features

In this step, data records change by adding missing values, converting, normalising, or scaling the data. Moreover, these processes use dummy variables. This is a crucial step in feature engineering for machine learning.

4. Construction Features

Both automatically and manually building features is possible. In automatic processes, techniques like PCA, tSNE, or MDS (linear and nonlinear) will be beneficial. There are countless alternatives when it comes to manual feature construction. The approach you choose depends on the issue at hand. Convolution matrices are among the most popular solutions. They are also capable of developing new features while tackling computer vision issues.

5. Choosing Features

Feature selection or variable selection or attribute selection is a technique for reducing the number of input variables (feature columns). This step in feature engineering in ML helps avoid unuseful data. So, you can go ahead by selecting the most closely matching variable you are trying to predict. Therefore, you can eliminate unnecessary data, 

6. Evaluation and Validation of the Model

Determine the model's accuracy on the training set of data using the set of selected features. Further, move on to model verification if the model has the proper degree of accuracy. If not, evaluate your list of attributes and go back to the feature selection procedure.

Common Examples of Feature Engineering for Machine Learning

Below are some of the real-time feature engineering examples:
 

1. Using the same data to gain more insights

Variables like date, distance, age, weight, and so on are present in many datasets. Thus, to get the answers you need, though, it is best to convert them into different formats. For instance, weight by itself may not be useful for your analysis. However, you get a different picture if you convert your data into BMI.
 

2. Malware detection

Manually detecting malware is challenging. Also, neural networks are not always efficient. However, a hybrid strategy that starts with feature engineering is also an option. It allows you to draw attention to particular classes and structures that the ML model should watch out for. Feature engineering and machine learning applications are two closely related but distinct aspects of the machine learning (ML) process.

Feature Engineering Techniques

Some of the popular feature engineering techniques for machine learning include:
 

1. Imputation

Feature engineering addresses issues like inaccurate data, missing values, human error, general errors, or insufficient data sources. Moreover, missing values in the dataset have a significant impact on how well the algorithm performs. Hence the "Imputation" technique addresses the irregularities in a dataset.
 

2. Handling Outliers

Outliers are incorrect values or data points that occur too far from other data points. Moreover, they negatively impact the model's performance. However, this feature engineering technique allows for the handling of outliers. This method first detects the outliers before eliminating them.
 

3. Log transform

The logarithm transformation or the log transform, is one of the mathematical techniques common in ML. Moreover, the log transform can handle skewed data and enhance the distribution's approach to normality after transformation. Also, a model is substantially more trustworthy when the magnitude differences are normalised. Thus, it decreases the effect of outliers on the data.
 

4. Split Feature

The feature split method, as the name implies, involves dividing existing features into two or more sections. Further, it combines them to create new features. This feature engineering for machine learning method aids the algorithms in interpreting and picking up on the dataset's trends.
 

5. Binning

Overfitting, which happens by parameters and noisy input, is one of the primary problems in ML. It reduces the performance of the model. Also, it can normalise the noisy data using one of the common feature engineering approaches called "binning". 
 

6. One Hot Encoding

One hot encoding is the popular ML encoding technique. It is a process that turns category data into a form of machine learning algorithms. One can easily interpret and employ to provide precise predictions. It allows for the grouping of categorical data without compromising any information.

Conclusion

Feature engineering for machine learning is a crucial and very practical strategy for data scientists that can dramatically increase the effectiveness of ML models. Feature engineering helps to improve the accuracy and performance of the model. But there are more ways to increase prediction accuracy. Moreover, there are many more feature engineering methods available than those above. You can get more details and apply for our online certification program for machine learning.

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