Available Cores | Benchmarking

Example: Contingency-Tables | Data Preprocessing | Create Contingency Table | Convert Contingency Table to data.frame | Convert Contingency Table to kable | Percentages in cells | Example: Model Tables: Linear Regression | Create Three Models | Create Model Table | Convert Model Table to data.frame | Convert Model Table to kable | Example: Model Tables: Logistic Regression | Example: Model Tables: GLMM

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Comparison with Cox Proportional Hazards Regression | Test Fold Equality | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

General Overview | Steps to Prepare an Algorithm for Use with mlexperiments | The fit Method | The predict Method | The cross_validation Method | The bayesian_scoring_function Method | Finally, Create an R6 Class for the Learner | Examples | Preparations | Hyperparameter Tuning | Bayesian Tuning | Grid Search | Cross-Validation | Nested Cross-Validation | Inner Bayesian Optimization | Inner Grid Search

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Comparison with Logistic Regression | Test Fold Equality | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths

Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Comparison with Linear Regression | Test Fold Equality | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset

Introduction | Setup the prerequisites | Conduct the correction of experimental biases | Background information | General regression equations | Data dependent regression equations (experimental feature) | Selection of the correction algorithm | Outputs and Results | Regression plots

Load Data from dataquieR R package | Prepare DQAstats MDR | Read empty DQAstats MDR as template | Transform SHIP-MDR to DQAstats representation | Change Variable Type for Categorical Variables | Define Constraints | Constraints for Categorical Variables | Constraints for Continuous Variables | Constraints for String Variables | Add Plausibility Checks | Contraception in males (atemporal plausibility) | Diabetes age but no Diabetes (atemporal plausibility) | Every ID is associated with one Sex-value (uniqueness plausibility) | Display the prepared MDR | Create utils-folder | Run DQAstats::dqa() | Launch DQAgui as GUI-frontend to DQAstats

Supported Commit Types | Example | Further configurations