Example 1 | Example 2

Classification | Regression

2 - iris data

Random Forest | Logistic Regression with glmnet

Regression | Classification

Introduction | Setup | Data Preparation | Conclusion | Session Info

Introduction | Setup | Data Preparation | Ridge Regression with Cross-Validation | Session Info

Introduction | Setup | Data Preparation | Ridge Regression with Cross-Validation | Session Info

Introduction | Setup | Data Preparation | Ridge Regression with Cross-Validation | Session Info

Introduction | Setup | Data Preparation | Conclusion | Session Info

Introduction | Setup | Data Preparation | Linear Regression | Ridge Regression with Cross-Validation | Visualization | Model Comparison | Conclusion | Session Info

0 - Packages and data | 1 - update RVFL model using Polyak averaging | 2 - update RVFL model using Polyak averaging (Pt.2)

Classification | Regression

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Example 2: Boston Housing Price Prediction | Example 3: Economic Indicators (Longley dataset) | Example 4: US Crime Rate Analysis | Example 5: Car Price Analysis (Cars93 dataset)

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Make predictions | Compare predictions | Example 2: Boston Housing Price Prediction | Make predictions and compare | Example 3: Car Price Analysis (Cars93 dataset)

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Make predictions | Compare predictions | Example 2: Boston Housing Price Prediction | Make predictions and compare | Example 3: Economic Indicators (Longley dataset) | Example 4: US Crime Rate Analysis | Example 5: Car Price Analysis (Cars93 dataset)

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Example 2: Boston Housing Price Prediction | Example 3: Economic Indicators (Longley dataset) | Example 4: US Crime Rate Analysis | Example 5: Car Price Analysis (Cars93 dataset)

1 - ovarian dataset | 2 - PBC dataset | 3 - CoxExample dataset

1 - Regression | 1 - 1 Regression on Boston data set | 1 - 2 Regression on mtcars data set | 2 Classification | 2 - 1 Binary classification | 2 - 2 multiclass classification

0 - Install ahead | 1 - Prediction intervals based on Gaussian distribution (meh, but quick) | 1 - 1 With default parameters | 1 - 2 With external regressors | 1 - 3 With external regressors and K-Means clustering | 1 - 4 With K-Means clustering | 2 - Prediction intervals based on independent bootstrap | 2 - 1 With default parameters | 2 - 2 With external regressors | 2 - 3 With external regressors and K-Means clustering | 2 - 4 With K-Means clustering | 3 - Prediction intervals based on block bootstrap | 3 - 1 With default parameters | 3 - 2 With external regressors | 3 - 3 With external regressors and K-Means clustering | 3 - 4 With K-Means clustering | 3 - 5 Using the median instead of the mean in bootstrap aggregation | 4 - Prediction intervals based on moving block bootstrap | 4 - 1 With default parameters | 4 - 2 With external regressors | 4 - 3 With external regressors and K-Means clustering | 4 - 4 With K-Means clustering | 4 - 5 Using the median instead of the mean in bootstrap aggregation | 5 - Prediction intervals based on R-Vine copula simulation

Univariate | AirPassengers | USAccDeaths | Multivariate

Univariate time series | Multivariate time series

Install ahead | Forecasting and plot predictions

Install ahead | ahead::loessf on Nile dataset

0 - Install ahead | 1 - Get and transform data | 2 - Risk-neutralize simulations | 2 - 1 Yield to maturities (fake risk-free rates) | 2 - 2 Risk-neutralized simulations | 3 - Visualization

Example usage

Example usage

Example: Conformal Prediction with Out-of-Sample Coverage | Simulated Data | Fit Conformal Model | Generate Predictions and Prediction Intervals | Calculate Out-of-Sample Coverage Rate | Results | Example: Conformal Prediction with the MASS::Boston Dataset | Load the Data | Split the Data | Fit Conformal Model 1 | Generate Predictions and Prediction Intervals 1 | Calculate Out-of-Sample Coverage Rate 1 | Fit Conformal Model 2 | Generate Predictions and Prediction Intervals 2 | Calculate Out-of-Sample Coverage Rate 2

Example usage 1

Introduction | Basic Usage | Different Machine Learning Methods | Using caret Models | Customizing Mean and Residual Models

Example: Conformal Prediction with Out-of-Sample Coverage | Simulated Data | Fit Conformal Model | Generate Predictions and Prediction Intervals | Calculate Out-of-Sample Coverage Rate | Results | Example: Conformal Prediction with the MASS::Boston Dataset | Load the Data | Split the Data | Calculate Out-of-Sample Coverage Rate 1

============================================================================ | VOLATILITY SIMULATION (CIR PROCESS) | ASSET PRICE SIMULATION (GBM WITH JUMPS)

USAccDeaths (method="adj") | AirPassengers (method="adj") | USAccDeaths (method='adj') | AirPassengers (method='adj')

USAccDeaths (method='adj') | AirPassengers (method='adj')

USAccDeaths | AirPassengers

USAccDeaths | AirPassengers

Example

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Make predictions | Compare predictions | Example 2: Boston Housing Price Prediction | Make predictions and compare | Example 3: Car Price Analysis (Cars93 dataset)

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Make predictions | Compare predictions | Example 2: Boston Housing Price Prediction | Make predictions and compare | Example 3: Economic Indicators (Longley dataset) | Example 4: US Crime Rate Analysis | Example 5: Car Price Analysis (Cars93 dataset)

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Make predictions | Compare predictions | Example 2: Boston Housing Price Prediction | Make predictions and compare | Example 3: Economic Indicators (Longley dataset) | Example 4: US Crime Rate Analysis | Example 5: Car Price Analysis (Cars93 dataset)

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Make predictions | Compare predictions | Example 2: Boston Housing Price Prediction | Make predictions and compare

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Make predictions | Compare predictions | Example 2: Boston Housing Price Prediction | Make predictions and compare | Compare different activation functions | Simulation plots

Example 1: MPG Prediction (mtcars dataset) | Load and prepare data | Fit models | Make predictions | Compare predictions | Example 2: Boston Housing Price Prediction | Make predictions and compare | Example 3: Car Price Analysis (Cars93 dataset)

0 - Installing and loading packages | 1 - iris dataset | 2- wine dataset | 3 - Penguins dataset

0 - Installing and loading packages | 1 - iris dataset | 2- wine dataset | 3 - Penguins dataset

1 - Comb OLS | 2 - Comb Ridge

1 - Comb OLS | 2 - Comb Ridge

0 - Packages | 1 - Generic forecaster | 2 - Conformal prediction

0 - lm regression | 1 - ranger regression | 2 - KRR & ranger regression on Boston | 3 - KRR regression on mtcars | 4 - RVFL regression

0 - Packages and data | 1 - RVFL regression updates | 2 - update RVFL model using Polyak averaging

1 - Using Classifier object | 2 - ranger classification | 3 - extratrees classification | 4 - Penguins dataset

Example usage 1 | Example usage 2

Example usage 1 | Example usage 2 | Example usage 3 | Example usage 4

1 ranger regression | 2 - Using Classifier object

0 - Global parameters | 1 - Regression | least squares | glmnet | Random Forest | xgboost | boxplots for regression | 2 - classification | boxplots for classification

Regression | Create a dataset | Cross-validation for a few models | Time series | Classification