Description: Inductive bias refers to the assumptions that a machine learning algorithm makes to predict outputs for inputs it has not previously encountered. This concept is fundamental in machine learning, where a model is trained with a labeled dataset so that it can generalize and make predictions about unseen data. Inductive bias allows the model to simplify the complexity of the input space, facilitating the identification of patterns and relationships in the data. However, this bias can also lead to errors if the assumptions made are incorrect or if the training data is not representative of the real problem. Therefore, inductive bias is a delicate balance between the model’s ability to generalize and the accuracy of its predictions. An appropriate inductive bias can enhance the efficiency of learning and the quality of predictions, while an inadequate bias can result in overfitting or underfitting, negatively impacting the model’s performance in real-world situations.
