What is model errors?

Model errors refer to the discrepancies or deviations in the predicted values of a model from the actual values observed in a dataset. These errors can occur due to a number of reasons, such as:

1. Inaccurate Data: Model errors can occur when the data used to train the model is inaccurate or incomplete. For example, missing values or outliers in the training dataset can lead to errors in the model's predictions.

2. Underfitting: Underfitting occurs when a model is too simple to capture the complex relationships between the variables in the dataset. This can result in a high bias error, which means that the model is unable to make accurate predictions on the training data.

3. Overfitting: Overfitting occurs when a model is too complex and tries to fit the noise in the data, instead of the underlying patterns. This can result in a high variance error, which means that the model is unable to make accurate predictions on new data.

4. Model Assumptions: Model errors can occur when the assumptions made by the model do not hold true for the dataset. For example, linear regression assumes a linear relationship between the independent and dependent variables, which may not always be the case in real-world datasets.

5. Sampling Bias: Sampling bias occurs when the training data is not representative of the population from which it was sampled. This can result in a model that is biased towards certain groups or variables, leading to errors in the predictions.