In a case study, if a linear regression model has a high R-squared value but a h

In a case study, if a linear regression model has a high R-squared value but a high Mean Squared Error (MSE), what does this suggest?

In a case study, if a linear regression model has a high R-squared value but a high Mean Squared Error (MSE), what does this suggest?

Step 1: Understand R-squared. It tells us how well the model explains the variation in the data. A high R-squared means the model explains a lot of the data's variance.
Step 2: Understand Mean Squared Error (MSE). It measures how far off the model's predictions are from the actual values. A high MSE means the predictions are not very accurate.
Step 3: Analyze the situation. A high R-squared with a high MSE suggests that the model fits the training data very well but may not perform well on new, unseen data.
Step 4: Consider overfitting. This means the model is too complex and captures noise in the training data instead of the underlying pattern.
Step 5: Conclude that the model may need simplification or better validation to improve its generalization to new data.

No concepts available.