Publications by John Pauline Pineda
Methods : Resampling Procedures for Model Hyperparameter Tuning and Internal Validation
1. Table of Contents This document presents a non-exhaustive list of resampling procedures for hyperparameter tuning and internal validation as applied on a classification modelling problem using various helpful packages in R. 1.1 Sample Data The GermanCredit dataset from the caret package was used for this illustrated example. Prelimi...
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Methods : Modelling Numeric Responses for Prediction
1. Table of Contents This document presents a non-exhaustive list of modelling techniques for predicting numeric responses using various helpful packages in R. 1.1 Sample Data The Solubility dataset from the AppliedPredictiveModeling package was used for this illustrated example. Preliminary dataset assessment: [A] 1267 rows (observati...
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Methods : Modelling Dichotomous Categorical Responses for Prediction
1. Table of Contents This document presents a non-exhaustive list of modelling techniques for predicting dichotomous categorical responses using various helpful packages in R. 1.1 Sample Data The Solubility dataset from the AppliedPredictiveModeling package was used for this illustrated example. The original numeric response was transf...
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Methods : Modelling Multiclass Categorical Responses for Prediction
1. Table of Contents This document presents a non-exhaustive list of modelling techniques for predicting multiclass categorical responses using various helpful packages in R. 1.1 Sample Data The Solubility dataset from the AppliedPredictiveModeling package was used for this illustrated example. The original numeric response was transfo...
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Methods : Evaluating Hyperparameter Tuning Strategies and Resampling Distributions
1. Table of Contents This document presents a non-exhaustive list of evaluation techniques for hyperparameter tuning strategies and resampling distributions using various helpful packages in R. 1.1 Sample Data The Sonar dataset from the mlbench package was used for this illustrated example. Preliminary dataset assessment: [A] 208 rows...
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Methods : Remedial Procedures in Handling Imbalanced Data for Classification
1. Table of Contents This document presents a non-exhaustive list of remedial procedures applied for severe class imbalance using various helpful packages in R. 1.1 Sample Data The Sonar dataset from the mlbench package was used for this illustrated example. The original dataset was transformed to simulate class imbalance. Preliminary ...
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Methods : Cost-Sensitive Learning for Severe Class Imbalance
1. Table of Contents This document presents a non-exhaustive list of cost-sensitive learning procedures applied for severe class imbalance using various helpful packages in R. 1.1 Sample Data The Sonar dataset from the mlbench package was used for this illustrated example. The original dataset was transformed to simulate class imbalanc...
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Methods : Evaluating Model-Independent Feature Importance for Predictors with Numeric Responses
1. Table of Contents This document presents a non-exhaustive list of feature importance metrics for predictors with numeric responses using various helpful packages in R. 1.1 Sample Data The Solubility dataset from the AppliedPredictiveModeling package was used for this illustrated example. Preliminary dataset assessment: [A] 1267 row...
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Methods : Evaluating Model-Independent Feature Importance for Predictors with Dichotomous Categorical Responses
1. Table of Contents This document presents a non-exhaustive list of feature importance metrics for predictors with dichotomous categorical responses using various helpful packages in R. 1.1 Sample Data The Solubility dataset from the AppliedPredictiveModeling package was used for this illustrated example. The original numeric response...
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Methods : Selecting Informative Predictors Using Recursive Feature Elimination
1. Table of Contents This document implements recursive feature elimination for selecting informative predictors using various helpful packages in R. 1.1 Sample Data The AlzheimerDisease dataset from the AppliedPredictiveModeling package was used for this illustrated example. Preliminary dataset assessment: [A] 333 rows (observations) ...
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