The most underestimated Machine Learning strategy is not what you think it is! 🤔📈💡
Many people assume that the key to success in Machine Learning is simply having more data, better models, or smarter algorithms. And while those things are certainly important, there's one strategy that's often overlooked or undervalued: 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴.
Feature Engineering is the process of selecting, transforming, and combining input variables (or "features") to create a more informative and predictive representation of the data. In other words, it's about using domain knowledge, creativity, and intuition to extract the most relevant and useful information from raw data.
Why is Feature Engineering so powerful? Here are just a few reasons:
• It can help reduce noise, redundancy, or irrelevant information in the data, making it easier for models to learn and generalize.
• It can reveal hidden patterns, relationships, or interactions among the features that would be difficult or impossible to detect with raw data alone.
• It can enable models to capture complex or nonlinear relationships between the input and output variables, leading to higher accuracy and better performance.
• Despite these benefits, many people still underestimate or overlook Feature Engineering, either because they don't have enough domain knowledge, they don't want to invest the time and effort, or they simply don't realize its potential.
So if you're looking to improve your Machine Learning skills and get better results, don't overlook the power of Feature Engineering! Whether you're working with structured or unstructured data, classification or regression tasks, or deep learning or traditional models, Feature Engineering can make a big difference.
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Head of Data @ Sephora | RecSys | DE | NLP | CV | GCP | AWS | Neural Search | MLOps | Data Centric AI | DS Instructor | Writer
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Was waiting for the kindle version! Getting it soon! Could you pls post the link here? (Want to avoid fake stuff)