M CHOLogistic RegressionLogistic regression is a model that is very well suited to problems where we have a binary output. That is, we are trying to use certain…10h ago10h ago
M CHOTensorflow light - Optimization Techniques on mobile and embedded devicesThere are several methods that one can use to achieve these types of optimizations and these include quantization, which reduces the…Jul 22Jul 22
M CHOBayesian OptimizationBayesian Optimization concerns the problem of maximizing expensive black-box functions.Jul 20Jul 20
M CHONaive BayseNaive Bayes is a machine learning algorithm that leverages probabilistic methods and techniques for solving supervised learning problems…Mar 19Mar 19
M CHOEnsemble treesAn alternative approach to avoid overfitting that is often more powerful than tree pruning: tree ensembles.Mar 11Mar 11
M CHODecision treesA decision tree is a supervised machine learning algorithm that is used for both regression and classification tasks. It is a…Dec 5, 2023Dec 5, 2023
M CHOK-Nearest NeighborsThe bias of k-Nearest Neighbor methods is the assumption that similar inputs lead to similar outputs. We need to understand how we measure…Nov 27, 2023Nov 27, 2023
M CHOFine-tuning algorithms — k-fold cross-validationThe training set–validation set approach has two significant shortcomings:Nov 16, 2023Nov 16, 2023
M CHOFine-tuning algorithms — oversamplingOversampling allows you to pick out cases of interest with higher sensitivity.Nov 16, 2023Nov 16, 2023
M CHOPerformance measures for classification problemsConfusion Matrix is a performance measurement for the machine learning classification problems where the output can be two or more classes…Nov 5, 2023Nov 5, 2023