Inductive Bias

• Set of assumptions that the learner uses to predict outputs of given inputs that it has not yet encountered
• In Bayesian
  • Bayesian Prior can shape the Bayesian Posterior in the way that it can be a similar distribution to the former
• In KNN
  • we assume that similar data points are clustered near each other away from the dissimilar ones
• in LinearRegression
  • we assume that the variable Y is linearly dependent on the explanatory variables X.
  • Therefore, the resulting model linearly fits the training data. However, this assumption can limit the model’s capacity to learn non-linear functions.
• in Logistic Regression
  • assume that there’s a hyperplane that separates the two classes from each other. This simplifies the problem, but one can imagine that if the assumption is not valid, we won’t have a good model.
• Non Relational Inductive Bias
• Relational Inductive Bias