Fitting a model too closely to sample data, resulting in a model that does not accurately reflect the population is termed as a. approximation. b. overfitting. c. postulating. d. hypothesizing.

Over fitting  

Explanation:

Over fitting is the situation where the model is well fitted with the current data but not fitted for sample data or not able to predict the sample data. This has happened when it is over-relied on the sample pattern and the trends that are going on in trends. The model is only applicable for new sample lonely. The model does not have access to this type of anomalous.

For example the housing data. Many times the model is so flexible and highly liked by the over fitted data.

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