What is Feature Selection and Engineering?

Feature selection and engineering is the process of selecting and creating the right features to use in machine learning algorithms. It involves deciding which features are important and useful, and then creating new features to improve the accuracy of the model.

Think of it like this: you have a box full of tools and you need to pick the right ones that are best suited to build something. In machine learning, the “box” is a set of data and the “tools” are the features.

Why is Feature Selection and Engineering Important?

Feature selection and engineering are important because if you don’t pick the right features, your machine learning algorithm won’t work properly. For example, if you are trying to build a model to predict the price of a house, you need to select the right features such as size, location, number of bedrooms, etc. If you don’t, your model won’t be able to accurately predict the price.

In addition, some machine learning algorithms can take a long time to run if they are given too many features. By reducing the number of features, you can reduce the running time and make it more efficient.

How to Select and Engineer Features

There are several different ways to select and engineer features. One way is to use domain knowledge. For example, if you are building a model to predict the price of a house, you know that size and location are important features.

Another way is to use feature selection algorithms such as correlation and mutual information. These algorithms measure the correlation between the features and the target variable, and select the ones that are the most relevant.

Finally, you can create new features by combining existing ones. For example, if you have the features “size” and “location”, you can create a new feature called “location/size” by dividing the two. This can help improve the accuracy of the model.

Example of Feature Selection and Engineering in Business

Let’s look at a practical example of feature selection and engineering in business.

Imagine you are building a model to predict customer churn. To do this, you need to select the right features. You might decide to include features such as age, gender, average spending, and number of orders.

You can then engineer new features by combining existing ones. For example, you can create a new feature called “spending/orders” by dividing the two. This can help you better understand customer behavior and improve the accuracy of the model.

Conclusion

Feature selection and engineering are important steps in the machine learning process. It involves deciding which features are important and creating new features to improve the accuracy of the model.

When building a machine learning model for your business, it is important to choose the right features and engineer new ones if necessary. This will ensure that the model is accurate and efficient.

Do you think feature selection and engineering is an important step in machine learning? Why or why not?

Got a question? Send it here.