You're facing pressure to speed up model building. How do you balance it with thorough feature engineering?
In the fast-paced world of data science, the pressure to deliver predictive models quickly can often clash with the need for thorough feature engineering. As a data scientist, you might find yourself torn between the demands for speed and the pursuit of accuracy. Feature engineering is the process of selecting, modifying, or creating variables, known as features, that will be used to train a machine learning model. It's a critical step because the quality and relevance of these features can significantly impact the model's performance. Balancing rapid model development with detailed feature engineering requires a strategic approach, prioritizing efficiency without compromising on the integrity of your model.
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Kaibalya BiswalAlways a Learner----- Tech fanatic 💻 || Guiding and Mentoring || Data Science & ML , Tableau, Statistics || Kaggle…
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Tavishi Jaglan3xGoogle Cloud Certified | Data Science | Gen AI | LLM | RAG | LangChain | ML | Mlops |DL | NLP | Time Series Analysis
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Harsh DhimanConsultant | Data Scientist at EY | Industrial LLMs | Predictive Maintenance