Have you heard about Feature Engineering?

By NIIT Editorial

Published on 17/01/2022

6 minutes

Feature engineering is a machine learning method that uses data to generate new variables that were not present in the training set. It can generate new features for supervised and unmonitored learning to standardize and speed up data transformations while improving model accuracy. When operating with machine learning models, feature engineering is required. A bad feature will have a direct impact on your model, regardless of the data or architecture. 

Let's look at a simple example to help you understand it better. The prices of properties in x city are listed below. In addition, it displays the size of the house as well as the total cost. This new feature will allow us to learn a lot about our data. As a result, we now have a new column that shows the cost per square foot. There are three primary methods for locating any error:

1.     You can utilize Domain Knowledge to get in touch with a property advisor or property manager and show him the price per square foot.

2.     If your lawyer tells you that the price per square foot cannot be less than $3400, you may have a problem.

3.     The data can be represented graphically.

When you plot the data, you'll notice that one price differs significantly from the others. The problem is easily visible in the visualization method. The third method is to use Statistics to analyze your data and identify any issues. Thus, feature engineering is made up of several processes.

Creating Features: Creating features entails creating new variables that will be most useful to our model. This can include adding or removing features. For example, the cost per square foot column was added as a feature, as we saw above.

Transformations: A feature that transforms characteristics from one representation to another is known as a feature transformation. The primary objective here is to plot and display information; if something doesn't add up with the new features, we can reduce the number of features used, speed up training, or improve the accuracy of a specific model.

Feature Extraction: Feature extraction identifies useful information by extracting features from a data set. This compresses the amount of data into controllable quantities for algorithms to handle without distorting the original connections or significant information.

Exploratory Data Analysis (EDA): Exploratory data analysis (EDA) is a powerful optimization tool for understanding your statistics by exploring its properties. The technique is frequently used when the goal is to generate new hypotheses or discover patterns in data and Deep learning. In addition, it is frequently used on large amounts of previously unanalyzed qualitative or quantitative data.

Benchmark: A Benchmark Prototype is the most user-friendly, dependable, transparent, and easy to interpret model against whom your own can be measured. Test sets of data to see if your new machine learning outperforms an acknowledged benchmark. These benchmarks are frequently used to compare the performance of different machine learning models such as neural networks and support vector machine svm, linear and non-linear classifiers, or approaches such as clustering algorithms.

The Importance of Feature Engineering 

Feature engineering is a critical stage in machine learning. The process of designing artificial characteristics into an algorithm is referred to as feature engineering. These artificial characteristics are then used by the algorithm to improve productivity or to yield better results. Data scientists spend most of their time working with data, so their models must be accurate.

Machine Learning Feature Engineering Steps

Let's take a look at some of the best feature engineering techniques you can employ. Some of the techniques listed may be more effective with specific algorithms or datasets, whereas others may apply in all situations.

1. Imputation

Missing values are one of the most common issues in preparing your data for machine learning. Human errors, interruptions in the data flow, privacy concerns, and other factors may all contribute to missing values. However, for whatever reason, missing values affect the performance of machine learning models.

2. Handle Outliers

Outlier handling refers to the process of removing outliers from a dataset. This method can be applied to a wide range of scales to produce accurate data representation. This affects the performance of the model. The effect may be large or small depending on the model.

3. The Log Transform

The most commonly used technique among data scientists is the log transform variation. It is most commonly used to transform a skewed distribution into a normal or less-skewed distribution.

4. One-time encoding

A one-hot encoding is a form of encoding in which a component of a finite set is depicted by its index, only with one element having its index set to "1" and other elements having indices in the range [0, n-1]. Unlike binary encoding schemes, in which each bit can represent two values (i.e., 0 and 1), this strategy assigns a unique value to each possible scenario.

5. Scaling

Feature scaling is among the most prevalent and difficult problems in machine learning, but it is also critical. To train prediction models, we need data with a recognized set of features that can be scaled up or down as needed.


The introduction of innovative data features from raw data is known as feature engineering. Engineers use this technique to examine raw data and potential information to extract a new or even more beneficial set of features. Feature engineering can be thought of as a generalization of optimization techniques that allows for more accurate analysis. The best data science courses online are available now on the internet.

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