R package of the Approximate Best Subset Maximum Binary Prediction Rule (PRESCIENCE) proposed by Chen and Lee (2018).
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Updated
Nov 1, 2018 - R
R package of the Approximate Best Subset Maximum Binary Prediction Rule (PRESCIENCE) proposed by Chen and Lee (2018).
This project is on Data Mining process using R depending on ISLR book.
Data Analytics and Machine Learning in R. Linear-regression, Logistic-regression, Hierarchical-clustering, Boosting, Bagging, Random-forests, K-means-clustering, K-nearest-neighbors (K-N-N), Tree-pruning, Subset-selection, LDA, QDA, Support Vector Machines (SVM)
This aims to run a subset selection process on the data set using R
Designing Industrial Experiments, one-way, and two-way ANOVA analysis, Experimental design principles (Replication, Randomization, and Blocking), Parameter Estimation, Sample Variance
Find the simplest model and the best method to predict whether an observation belongs to categories LOWER/GREATER of 20% trimmed mean of "ncrim" variable. AUEB Computer Science course Statistical Learning.
Neighbourhood Functions for Local-Search Algorithms
A summative coursework for MAS8404 Statistical Learning for Data Science
Fast Backward Elimination in R
Efficient Variable Selection for GLMs in R
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