AKAzmat Khaninazm999.hashnode.dev·Aug 27, 2023 · 4 min readGSOC'23: Final ReportProject Details Title: Addition of regression GAM and kNN classification functionality in statistics package Organization(s): GNU Octave, Google Summer of Code Mentors: Andreas Bertsatos and Nick Jankowski Community Discussion thread : [ Discourse Th...00
AKAzmat Khaninazm999.hashnode.dev·Aug 26, 2023 · 5 min readGSOC'23 Week 9 & 10: Implementing Classdef for GAM and comparision with other softwaresClassdef in Octave For Matlab compatibility and better representation of GAM model we will need a classdef RegressionGAM for storing the training data and other model parameters. The classdef here would serve as a Representation of the Training data,...00
AKAzmat Khaninazm999.hashnode.dev·Jul 31, 2023 · 1 min readGSOC'23 Week 8: GAM Regression Part 2Predicting Values from fitted model Untill Now we have Fitted the Models for Two different functions for 100 synthetic sample values with added noise. $$f_1(x) = sin (3x).$$ and $$f_2(x) = x^3$$ Now when the backfitting algorithm converges, the last ...00
AKAzmat Khaninazm999.hashnode.dev·Jul 24, 2023 · 4 min readGSOC'23 Week 6&7: GAM Regression Part 1why GAMs ? A Linear Model is Represented as : $$Y=Xβ+ε$$where Y is the Response Variable, X is the predictor data, Beta is the regression Coefficient, and epsilon is the error term. Linear models are known to be all-in-one models when it comes to dat...00
AKAzmat Khaninazm999.hashnode.dev·Jul 3, 2023 · 6 min readGSoC'23 Week 4: Implementing kNN algorithm for classificationkNN Algorithm in the parametric approach, we assume an underlying relationship between the predictor (input) and response (output) values. $$Y = f(X, \theta) + \epsilon$$Y represents the response variable, X represents the predictor variable(s), f re...00
