FAQ on Machine Learning

When there is sufficient data ‘Isotonic Regression’ is used to prevent an overfitting issue. The method frequently used to prevent overfitting in machine learning is regularization. Regularization techniques add a penalty term to the model’s loss function, discouraging overly complex models that fit the training data too closely. There are different types of regularization methods, … Read more

The two methods used for predicting good probabilities in Supervised Learning are Platt Calibration Isotonic Regression These methods are designed for binary classification, and it is not trivial. In supervised learning, calibration refers to the process of mapping the model’s output probabilities to predicted probabilities that are more accurate and representative of the true likelihood … Read more

The process of selecting models among different mathematical models, which are used to describe the same data set is known as Model Selection. Model selection is applied to the fields of statistics, machine learning and data mining. Model selection in machine learning refers to the process of choosing the best model or algorithm for a … Read more

Inductive Logic Programming (ILP) is a subfield of machine learning which uses logical programming representing background knowledge and examples. Inductive Logic Programming (ILP) is a subfield of machine learning that focuses on learning logical representations from examples, typically expressed as first-order logic rules. The aim of ILP is to induce general theories or hypotheses from … Read more

Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results. Genetic Programming (GP) is a type of evolutionary algorithm used in the field of machine learning and artificial intelligence. It is inspired by the process of … Read more