FAQ on Machine Learning

In Machine Learning and statistics, dimension reduction is the process of reducing the number of random variables under considerations and can be divided into feature selection and feature extraction. Dimensionality reduction in machine learning is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. It can … Read more

PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction. PCA (Principal Component Analysis), KPCA (Kernel Principal Component Analysis), and ICA (Independent Component Analysis) are all dimensionality reduction techniques commonly used in machine learning and data analysis. Here’s a … Read more

Incremental learning method is the ability of an algorithm to learn from new data that may be available after classifier has already been generated from already available dataset. An Incremental Learning algorithm in ensemble refers to a machine learning approach where the model is updated incrementally as new data becomes available, rather than retraining the … Read more

The expected error of a learning algorithm can be decomposed into bias and variance. A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The variance term measures how much the learning algorithm’s prediction fluctuates for different training sets. In the context of machine learning, the bias-variance … Read more

The general principle of an ensemble method is to combine the predictions of several models built with a given learning algorithm in order to improve robustness over a single model. Bagging is a method in ensemble for improving unstable estimation or classification schemes. While boosting method are used sequentially to reduce the bias of the … Read more