Topic: bagged-forests Goto Github
Some thing interesting about bagged-forests
Some thing interesting about bagged-forests
bagged-forests,This project aims at developing, validating, and testing several classification statistical models that could predict whether or not an office room is occupied using several data features, namely temperature (âŚC), light (lx), humidity (%), CO2 (ppm), and a humidity ratio. The data is modeled using classification techniques i.e. Logistic regression, Classification tree, Bagging-Random forest, and Gradient boosted trees. These models were trained and then after evaluated against validation and test sets and using confusion matrices to obtain classification and misclassification rates. The logistic model was trained using glmnet R package, Tree package for classification tree model, randomForest for both Bagging and Random Forest Models, and gbm package for Gradient Boosted Model. The best accuracy was obtained from the Random Forest Model with a classification rate of 93.21% when it was evaluated against the test set. Light sensor is also the most significant variable in predicting whether the office room is occupied or not, this was observed in all the five models.
User: mirugwe1
bagged-forests,An iterative machine learning framework for predicting temperature profiles for an additive manufacturing process
Organization: nu-cucis
Home Page: http://cucis.ece.northwestern.edu
bagged-forests,This repository implements the basic machine learning classifiers for the problem of Yelp reviews classification. We assume the problem to be a binary classification problem. The models implemented are Naive Bayes, Logistic Regression, Support Vector Machine (linear), Decision Trees, Bagged Decision Trees, Random Fforests, and Boosted Decision Trees.
User: sakbarpu
bagged-forests,Assignments and Project from NJIT CS 675
User: thekishanpatel
bagged-forests,Collection of code covering various topics in Machine Learning
User: worldofnick
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