If a model gives good results on the in-sample data but fails to be generally applicable to out-of-sample data, it is referred to as Overfitting.
Overfitting. Understanding Overfitting Overfitting is a situation where the data used for training is the "best". So that if a test is carried out using different data it can reduce accuracy (results that are made are not as expected). Overfitting can occur when some constraints are based on special properties that don't make a difference to the data. Besides that, excessive duplication of minor data can also result in overfitting. To overcome the problem of Overfitting or Underfitting, there are several ways you can try:
• Double-check the validity of the data set.
• Use the resampling technique to estimate model accuracy. Where later it will validate several times with different data comparisons until it finds optimal accuracy.
Learn more about overfitting at https://brainly.com/question/5008113.
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