Feature Engineering- Feature Selection, Feature Transformation and Feature Extraction
If you find yourself confused with the terms related to feature engineering, worry not! Today, we will learn these concepts while making an extravaganza pizza ;) ;)
Feature engineering is a process where we transform raw data into relevant and informative features that can be used as input for machine learning algorithms. The process of using practical, statistical and data science knowledge to select, transform, or extract characteristics, properties, and attributes from raw data.
General Categories:
1. Feature Selection 2. Feature Transformation 3. Feature Extraction
1. Let’s start with feature selection: In my kitchen, which serves as a database filled with loads of data, I need specific ingredients like flour, yeast, baking powder, corn, capsicum, onion, olive oil, mozzarella cheese, salt, sugar, and more for the extravaganza pizza. So, I select only the necessary items from the vast variety in my kitchen. This selection process is known as feature selection. Similarly, when building a model, we must understand which features are essential for model building and choose only those relevant ones.
Technically, feature selection is the process of picking variables from a dataset that will be used as predictor variables for the model. Using all features in a dataset might negatively affect performance by adding complexity and noise to the model.
2. Next up is feature transformation: Now, we chop onion, capsicum, mushroom, tomatoes, and olives to make them ready for use. This process is akin to feature transformation, where the raw ingredients are converted into a format that is ready to be cooked.
Technically, feature transformation involves modifying existing dataset features to better suit the model training. Techniques like log normalization, standard scaling, and encoding are used for this. Don’t worry, these techniques may sound complex, but they are quite easy to explore and learn.
Technical Details of Feature Transformation.
3. Feature Extraction: Moving on, let’s prepare the sauce to add to the pizza. We add olive oil, garlic, crushed tomatoes, oregano, salt, and pepper, boil for a few minutes, stir and finally, the sauce is ready. This particular process can be likened to feature extraction, as it involves creating something new and flavorful from the existing ingredients.
From a technical perspective, feature extraction involves generating novel features from existing ones, with the primary objective of enhancing the predictive power of the model.
I hope these concepts related to Feature Engineering are now easier for you to grasp, and I’m confident that you won’t be confused about them again. Enjoy the learning process always.. :) :)
Complete Explanation of Feature Engineering in my channel.
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