**Explain BIAS:** In machine learning, “bias is a learner’s tendency to consistently learn the same wrong thing. Variance is the tendency to learn random things irrespective of the real signal.... It’s easy to avoid overfitting (variance) by falling into the opposite error of underfitting (bias). Simultaneously avoiding both requires learning a perfect classifier, and short of knowing it in advance there is no single technique that will always do best (no free lunch).”[domingos] See also variance, overfitting, classification.

Different definitions in web development like **bias** in Dictionary B.

- Manual Big Data:
- Meaning a popular marketing buzz phrase, definitions have proliferated, but in general, it refers to the ability to work with collections of data that had been impractical before because of their volume bias.
- Manual Bayes' Theorem:
- Meaning Rule. An equation for calculating the probability that something is true if something potentially related to it is true. If P(A) means “the probability that A is true” and P(A|B) means “the bias.
- Manual Binomial Distribution:
- Meaning outcomes of independent events with two mutually exclusive possible outcomes, a fixed number of trials, and a constant probability of success. This is a discrete probability distribution, as opposed bias.
- Manual Backpropagation:
- Meaning algorithm for iteratively adjusting the weights used in a neural network system. Backpropagation is often used to implement gradient descent. See also neural network, gradient descent bias.
- Manual Bayesian Network:
- Meaning “Bayesian networks are graphs that compactly represent the relationship between random variables for a given problem. These graphs aid in performing reasoning or decision making in the face of bias.