What is feature engineering in machine learning?
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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.
Feature engineering is the process of creating, selecting, and transforming raw data into meaningful inputs that improve the performance of a machine learning model. In simple terms, it's about making the data more useful so the model can learn better and make more accurate predictions.
In machine learning, models don’t understand raw data like text, dates, or categories directly. Feature engineering helps convert this raw information into numerical or structured formats that algorithms can use.
Key Steps in Feature Engineering:
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Creating new features
Deriving new variables from existing data. For example, turning a date of birth into "age" or combining "height" and "weight" into a "BMI" feature. -
Transforming features
Scaling, normalizing, or encoding features (like converting categorical data into numbers using one-hot encoding). -
Selecting important features
Keeping only the most relevant variables and removing redundant or irrelevant ones to reduce noise and improve model performance. -
Handling missing data
Filling in or removing incomplete data entries to avoid training issues.
Why is it Important?
Good features can make a simple model perform incredibly well, while poor features can make even complex models fail. It’s often said that “better data beats fancier algorithms.
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