1) Bias in a machine learning model occurs when the predicted values are further from the actual values. Low bias indicates a model where the prediction values are very close to the actual ones.
2) Underfitting: High bias can cause an algorithm to miss the relevant relations between features and target outputs.
3) Variance refers to the amount the target model will change when trained with different training data. For a good model, the variance should be minimized.
4) Overfitting: High variance can cause an algorithm to model the random noise in the training data rather than the intended outputs.