What Bias and Variance Tell About a Machine Learning Model?

• Bias is the difference between predicted values and expected results.

• Variance is the variability of your model's predictions over different sets of data.

• A machine learning model with a low bias is a perfect model and a model with a high bias is expected with a high error rate on the training and test sets.

• A machine learning model with high variance indicates that the model may work well on the data it was trained on, but it will not generalize well on the dataset it has never seen before.