In supervised learning, if the model's predicted output is very different from the actual training data output, this is known as
Question
In supervised learning, if the model's predicted output is very different from the actual training data output, this is known as
Solution
In supervised learning, if the model's predicted output is very different from the actual training data output, this is known as a high error rate or poor model performance. This discrepancy is often referred to as "residuals" in regression models. If these residuals are large, it indicates that the model is not accurately capturing the relationship between the input features and the output variable. This could be due to various reasons such as overfitting, underfitting, or the model not being complex enough to capture the underlying patterns in the data.
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