Here, the red dashed line is the model’s output while the blue crosses are actual data samples.
1. The model’s output does not match the true function at all. Hence the model is said to be underfitting and its accuracy is lower.
2. In the second case, model performance is trying to cover all the data samples even if they are out of alignment with the true function. This model is said to be overfitting and this too has a lower accuracy.
3. In the third one, the model’s performance matches well with the true function which states that the model has optimum accuracy and the model is called a perfect fit.
Study more about Natural Language Processing at Natural Language Processing Class 10