Comparison of regression

January 23, 2019

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Evaluate Bayesian Linear Regression, Neural Net. Regression, Boosted Decision Tree Regression, Linear Regression, and Decision Forest Regr.
1. Data Boston upload from library(MASS) 2. Split data 50% train data and 50% test data. 3. Train data use medv as label 4. Model Bayesian Linear Regression Neural Network Regresiion Boosted Decision Tree Regression Linear Regression Decision Forest Regression 5. Score Model 6. Evaluate Model 7. Execute R Script For Bayesian Linear Regression upload R script: >dataset <- maml.mapInputPort(1) >data.set <- data.frame(Algorithm="Bayesian Linear Regression") >data.set <- cbind(data.set, dataset[2:6]) > maml.mapOutputPort("data.set"); For Neural Net. Regression, Boosted Decision Tree Regression, Linear Regression, the R script: >dataset <- maml.mapInputPort(1) >data.set <- data.frame(Algorithm="Neural Net.work Regression") >data.set <- cbind(data.set, dataset[1:5]) > maml.mapOutputPort("data.set"); 8. Add Row, connected to all model. 9. Run, visualise the result. 10. Conclusion, model Boosted Decision Tree Regression is the best model with RMSE 2.790232 and Coefficient of Determination 0.898652.