Short: A model with minimal parameters may exhibit high bias and low variance, while a model with numerous parameters may demonstrate high variance and low bias. Therefore, it is essential to achieve an optimal balance to avoid overfitting and underfitting the data. High bias arises from incorrect assumptions made by the learning algorithm, whereas variance arises from a model's sensitivity to minor variations in the training dataset.
Detail: During development, all algorithms exhibit some degree of bias and variance. Models can be adjusted to address either bias or variance, but it is impossible to reduce both to zero without adversely affecting the other. This introduces the concept of the bias-variance trade-off. Bias refers to the discrepancy between the average prediction of our model and the actual value being predicted, indicating the presence of systematic errors in the model. Every algorithm inherently possesses some level of bias due to assumptions made within the model to simplify learning the target function. High bias can lead to underfitting, where the algorithm fails to capture relevant relationships between features and target outputs. Simpler algorithms tend to introduce more bias, whereas nonlinear algorithms usually have lower bias. These errors can originate from various sources, including the selection of training data, feature choices, or the training algorithm itself. Variance measures how much a model's predictions change with different training sets, indicating the degree of over-specialization to a particular training set (overfitting). The goal is to assess the deviation of our model from the best possible model for the training data. The ideal model seeks to minimize both bias and variance, achieving a balance that is neither too simple nor too complex, thereby yielding minimal error. Low-variance models typically have a simple structure and are less sophisticated, but they risk being highly biased. Examples include Regression and Naive Bayes. Conversely, low-bias models generally have a more flexible and complex structure but are prone to high variance. Examples include Nearest Neighbors and Decision Trees. Overfitting arises when a model is overly complex and learns the noise in the data rather than the actual signals.