What are Features?
Features are nothing but the independent variables in machine learning models. What is required to be learned in any specific machine learning problem is a set of these features (independent variables), coefficients of these features, and parameters for coming up with appropriate functions or models (also termed as hyperparameters). The following represents a few examples of what can be termed as features of machine learning models:
A model for predicting whether the person is suitable for a job may have features such as:
* number of years of experience
* experience working in the field etc.
A model for predicting the size of a shirt for a person may have features such as age, gender, height, weight, etc.
Features can be in the form of raw data that is very straightforward and can be derived from real-life as it is. However, not all problems can be solved using raw data or data in its original form.
A Primer on Feature Engineering
Feature engineering is the process of selecting, interpreting, and transforming structured or unstructured raw data into attributes (features) that can be used to build effective machine learning models which more accurately represent the problem at hand. In this context, a “feature” refers to any quantifiable unique input that may be used in a predictive model, […]Read More
How to design a Feature Store for Sub-ML?
Let’s assume you want to leverage data to improve one of your processes, such as partner benchmarking. Even though it’s one of your top priorities for the year, you have limited resources to spend on partner data collection, segregation, and overall data preparation to do any sort of analysis. And even if you find a […]Read More
Data Shifts Power Within Organizations
A major challenge in going more data-driven in organization has less to do with data itself, and more to do with the ability to manage the dynamics that emerge as decision makers look at data as an input to decision process. I have a particular kind of power shift in mind. I am not referring […]Read More
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