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Model Validation with Techniques

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Model validation techniques: building trustable machine learning models Model validation serves as the very backbone of establishing trustable, effective machine learning models. One must ensure the model is good at performing its job not just on the training data but even on unseen data in general. To do this would mean avoiding not only overfitting but underfitting to make sure the model generalizes well to unseen data. Why Model Validation is Important 1. Reliability: Model validation gives you an assurance of the reliabilities and accuracy of your model. Once your model has been validated, you can be rested assured that it will work satisfactorily in real-world applications. 2. Trust: In general, model validation induces confidence among the stakeholders. It gives explanation and transparency to decision-making, which is relatively easy to gain the trust of the users and stakeholders as well. 3. Scalability: The more likely that a validated model will perform well in other settings...