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.
Bias is a phenomena in which the outcome of an algorithm is skewed in favour of or against a particular notion. Bias is a type of systematic inaccuracy that happens in machine learning models as a result of faulty assumptions made during the learning process.
Because models with high bias are too basic, increasing the degree of the polynomial in the hypothesis function might enhance the complexity, reducing bias.
In supervised machine learning, when an algorithm learns from training data or a sample data set of known quantities, bias and variance are utilised. Building machine-learning algorithms that produce accurate results from their models requires the right mix of bias and variance.
Individuals may also introduce biases by training and/or validating machine learning algorithms with incomplete, incorrect, or biassed data sets. Stereotyping, bandwagon effect, priming, selective perception, and confirmation bias are examples of cognitive bias that can mistakenly alter algorithms.