Here, the red dashed line is model’s output while the blue crosses are actual data samples.
● The model’s output does not match the true function at all. Hence the model is said to be under fitting and its accuracy is lower.
● In the second case, model performance is trying to cover all the data samples even if they are out of alignment to the true function. This model is said to be over fitting and this too has a lower accuracy
● 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.