What are Features in Machine Learning and Why it is Important?

In machine learning, features are individual independent variables that act like a input in your system. Actually, while making the predictions, models use such features to make the predictions. And using the feature engineering process, new features can also be obtained from old features in machine learning.

To understand in more simple way, lets take an example, where you can consider one column of your data set to be one feature which is also know as “variables or attributes” and the more number of features are known as dimensions. And depending on what you are trying to analyze the features you include in your dataset can vary widely.

What is Feature Engineering in Machine Learning?

Feature engineering is the process of using the domain knowledge of the data to create features that makes machine learning algorithms work properly. If feature engineering is performed properly, it helps to improve the power of prediction of machine learning algorithms by creating the features using the raw data that facilitate the machine learning process.

Why Feature is Important in Machine Learning?

Features in machine learning is very important, being building a blocks of datasets, the quality of the features in your dataset has major impact on the quality of the insights you will get while using the dataset for machine learning.

However, depending on the different business problems in different industries it is not necessary the features should be same features, so here you need to strongly understand the business goal of your data science project.

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Where on the other hand, using the “feature selection” and “feature engineering” process you can improve the quality of your datasets’ features, which a very tedious and difficult process. It these techniques are working well, you will get optimal dataset with all of the important features, that bearing on your specific business problem leads to the best possible model development and the most beneficial visual perception.

Tops Methods of Feature Selection in ML:

  • Universal Selection
  • Feature Importance
  • Correlation Matrix with Heatmap

Feature engineering is the most important part of machine leaning that makes difference between and good and bad model. And there are several steps involved in feature engineering and most preferred steps are given below.

Steps To Do Feature Engineering in ML:

  1. Gathering Data
  2. Cleaning DATA
  3. Feature Engineering
  4. Defining Model
  5. Training & Testing of model prediction

To perform the feature engineering in machine learning you need data experts like data scientists or hire machine learning engineer who can understand and perform the feature engineering process with right instructions. Cogito is one the companies providing the hiring and recruitment services with outsourcing of data scientists and machine learning engineers for in-house AI development or for remote locations as per the requirements of various companies.


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