The relationship between bias and variance is inverse. It is impossible to have a low bias and low variance ML model. When a data engineer tweaks an ML algorithm to better fit a specific data set, the bias is reduced, but the variance is increased.
The variance will increase as the model's complexity increases, while the bias will decrease. There is a higher level of bias and less variance in a basic model. To create an accurate model, a data scientist must strike a balance between bias and variance, ensuring that the model's overall error is kept to a minimum.
The model's simplifying assumptions simplify the target function, making it easier to estimate. Variance refers to how much the target function's estimate will fluctuate as a result of varied training data.
The term variance relates to how the model varies as different parts of the training data set are used. Simply said, variance refers to the variation in model prediction—how much the ML function can vary based on the data set.
Models with a high bias and a low variance are consistent but wrong on average. High Bias, High Variance: On average, models are wrong and inconsistent. Low Bias, Low Variance: On average, models are accurate and consistent.
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