Evaluating the machine learning model is very important to check the accuracy level and make sure this model will work well in real-life use. Evaluation means, checking the prediction of model after giving a raw data to recognize the data or object learn from previous machine learning training process.
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What is Cross Validation in Machine Learning and its Techniques?
Machine Learning (ML) model development is not complete until the model is validated to give an accurate prediction. The stability of the model is important, and it is important to rely on its decisions to be correct and unbiased, allowing trust in the model.
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