FAQ on Machine Learning

SVM has a learning rate and expansion rate which takes care of this. The learning rate compensates or penalises the hyperplanes for making all the wrong moves and expansion rate deals with finding the maximum separation area between classes. SVM (Support Vector Machine) is a supervised learning algorithm primarily used for classification tasks. It doesn’t … Read more

KNN is Supervised Learning where-as K-Means is Unsupervised Learning. With KNN, we predict the label of the unidentified element based on its nearest neighbour and further extend this approach for solving classification/regression-based problems. K-Means is Unsupervised Learning, where we don’t have any Labels present, in other words, no Target Variables and thus we try to … Read more

KNN is a Machine Learning algorithm known as a lazy learner. K-NN is a lazy learner because it doesn’t learn any machine learnt values or variables from the training data but dynamically calculates distance every time it wants to classify, hence memorises the training dataset instead. The machine learning algorithm known as the “lazy learner” … Read more

Variation Inflation Factor (VIF) is the ratio of variance of the model to variance of the model with only one independent variable. VIF gives the estimate of volume of multicollinearity in a set of many regression variables. VIF = Variance of model Variance of model with one independent variable. The Variance Inflation Factor (VIF) is … Read more

Variations in the beta values in every subset implies that the dataset is heterogeneous. To overcome this problem, we can use a different model for each of the clustered subsets of the dataset or use a non-parametric model such as decision trees. When the beta value for a certain variable varies significantly across different subsets … Read more