In this blog 🗒️ I will write about Feature engineering in machine learning and why it's important in terms of machine learning and also why we do feature engineering before training a ML model.
If you are learning 🧑💻 machine learning so you already know that features are input variables for our ML models and labels are the output variables. So, it should be easy to understand this topic or concept.
If we look the definition, it's saying - Feature engineering is the process of transforming raw data into features that are suitable for machine learning models.
In simple terms, we transform bad features into good features, we are enchanting the power of features (input variables) so our machine learning model performs efficiently and accurately. Understanding and learning feature engineering is really important concept for ML through feature engineering select only important data points or datasets from raw data and transform them in useful features.
Why we Feature Engineering in Machine Learning?
Go beyond basic reasons: Instead of just listing improved accuracy and efficiency, delve deeper into the benefits of feature engineering.
Reduced training time: Explain how engineered features simplify data for models, leading to faster training and lower computational costs.
Enhanced interpretability: Discuss how well-engineered features make models more transparent, allowing for better understanding of their reasoning and predictions.
Reduced model complexity: Illustrate how feature engineering can lead to simpler, more efficient models that are easier to maintain and improve.
This is it I hope you understand the concept of feature engineering and thanks for reading this article. Tell me in the comments 👋
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Have a look on a little playful explanation
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