Thread by @amitiitbhu

What is Feature Engineering for Machine Learning?

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#MachineLearning

Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work.

If feature engineering is done correctly, it increases the predictive power of machine learning algorithms by creating features from raw data that help facilitate the machine learning process.

The steps involved while solving any problem in machine learning are as follows:

- Gathering data.
- Cleaning data.
- [[ Feature engineering ]]
- Defining model.
- Training, testing model, and predicting the output.

Feature engineering is the most important art in machine learning which creates a massive difference between a good model and a bad model. Let's see what feature engineering covers.

Suppose, we are given the data "flight date-time vs status". Then, given the date-time data, we have to predict the flight's status.

The flight's status depends on the hour of the day, not on the date-time. We will create the new feature "Hour_Of_Day". Using the "Hour_Of_Day" feature, the machine will learn better as this feature is directly related to the flight's status.

Here, creating the new feature "Hour_Of_Day" is the feature engineering.

Let's see another example. Suppose we are given the latitude, longitude, and other data with the given label "Price_Of_House". We need to predict the price of the house in that area.

The latitude and longitude are not of any use if they are alone. So, here we will use the crossed column feature engineering. We will combine the latitude and the longitude to make one feature. Combining into one feature will help the model learn better.

Here, combining two features to create one useful feature is feature engineering.

Sometimes, we use the bucketized column feature engineering. Suppose we are given data in which one column is the age and the output is the classification(X, Y, Z).

By seeing the data, we realized that the output(X, Y, Z) is dependent on the age range like 11-20 years age-range output to X, 21-40 years output to Y, and 41-70 years output to Z. Here, we will create 3 buckets for the age-range 11-20, 21-40 and 41-70.

We will create the new feature which is the bucketized column "Age_Range" that has the numerical values 1, 2, and 3 where 1 is mapped to bucket 1, 2 is mapped to bucket 2, and 3 is mapped to the bucket 3.

Here, creating the Age_Range bucket is the feature engineering.

Sometimes, removing the unwanted feature is also feature engineering. As the feature which is not related degrades the performance of the model.

Now, the steps to do feature engineering are as follows:

- Brainstorm features.
- Create features.
- Check how the features work with the model.
- Start again from the first until the features work perfectly.

This is what we do in feature engineering.

That's it for now.

Happy Learning :)

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