Скачать с ютуб Model selection and Generalisation / KTU CS / Machine learning в хорошем качестве

Model selection and Generalisation / KTU CS / Machine learning

#bias #variance #generalisation #model #machinelearning #inductive When you train a machine learning algorithm with data it will become a model. Model selection is the process of choosing a right or optimal model from a set of candidate models given data. Dataset is divided into two subsets training set and validation set. Error in training data is called bias and error in testing is called variance. Generalization is how well a model trained on the training set predicts the right output for new instances. Two problems are underfitting and overfitting.