How does EDA contribute to the model building process in Machine Learning?
- By defining the ML algorithm to be used
- By fine-tuning the hyperparameters of the ML model
- By providing insights into the nature of data, and identifying trends and outliers
- By testing the performance of the ML model
EDA is integral to the model-building process in Machine Learning as it provides insights into the nature of the data, and identifies trends, patterns and outliers. These insights help to determine which Machine Learning models might be most appropriate to apply and can guide the feature engineering process.
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