Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. Here is a tutorial with audio, video, code, and slides: https://moudiki2.gumroad.com/l/nrhgb. 100 API requests are now (and forever) offered to every user every month, no matter the pricing tier.
In the new version of misc
, we introduce a conformalize
function (work in progress, along with predict
and simulate
S3 methods), which allows you to perform conformal prediction with any R machine learning model. Conformal prediction improves prediction intervals’ coverage rate thanks to held-out set cross-validation errors.
options(repos = c(techtonique = "https://r-packages.techtonique.net",
CRAN = "https://cloud.r-project.org"))
install.packages("misc")
Example: Conformal Prediction with Out-of-Sample Coverage
In this example, we demonstrate how to use the misc::conformalize
function to perform conformal prediction and calculate the out-of-sample coverage rate.
Simulated Data
We will generate a simple dataset for demonstration purposes.
set.seed(123)
n <- 200
x <- matrix(runif(n * 2), ncol = 2)
y <- 3 * x[, 1] + 2 * x[, 2] + rnorm(n, sd = 0.5)
data <- data.frame(x1 = x[, 1], x2 = x[, 2], y = y)
Fit Conformal Model
Now, we’ll use a linear model (lm
) as the fit_func
and its corresponding predict
function as the predict_func
.
library(misc)
library(stats)
# Define fit and predict functions
fit_func <- function(formula, data, ...) lm(formula, data = data, ...)
predict_func <- function(fit, newdata, ...) predict(fit, newdata = newdata, ...)
# Apply conformalize
conformal_model <- misc::conformalize(
formula = y ~ x1 + x2,
data = data,
fit_func = fit_func,
predict_func = predict_func,
split_ratio = 0.8,
seed = 123
)
Generate Predictions and Prediction Intervals
We will use the predict
method to generate predictions and calculate prediction intervals.
# New data for prediction
new_data <- data.frame(x1 = runif(50), x2 = runif(50))
# Predict with split conformal method
predictions <- predict(
conformal_model,
newdata = new_data,
level = 0.95,
method = "split"
)
head(predictions)
## fit lwr upr
## 1 1.6023773 0.5217324 2.683022
## 2 2.4634938 1.3828489 3.544139
## 3 0.6216433 -0.4590017 1.702288
## 4 0.9257140 -0.1549310 2.006359
## 5 2.0106565 0.9300115 3.091301
## 6 0.7427247 -0.3379203 1.823370
head(simulate(conformal_model, newdata = new_data, method = "kde")[,1:10])
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1.0061613 1.4378707 1.5956107 0.82351501 2.7246968 0.73219187 2.0356222 1.695236
[2,] 1.8008609 2.7971134 1.2861305 2.58125871 3.4363754 1.86727777 1.2363179 3.012968
[3,] 0.7061998 1.0880965 0.7643145 -0.01608328 0.6978976 0.08354196 1.2873470 1.644337
[4,] 1.6744387 2.1808671 1.3589588 0.71969680 0.6200716 0.41251896 0.2132685 1.143104
[5,] 2.5601307 2.6514539 0.9412205 1.71331614 1.8461498 2.50201601 1.6053888 2.244651
[6,] -0.1938776 0.6363327 0.6612391 0.95181269 2.2220346 1.91485674 1.5329600 1.151063
[,9] [,10]
[1,] 0.9646508 1.8197675
[2,] 2.8635302 2.1993619
[3,] 1.0299818 0.3076988
[4,] 1.7906682 0.7944157
[5,] 2.3608802 2.0952129
[6,] 0.5367075 1.9065546
Calculate Out-of-Sample Coverage Rate
The coverage rate is the proportion of true values that fall within the prediction intervals.
# Simulate true values for the new data
true_y <- 3 * new_data$x1 + 2 * new_data$x2 + rnorm(50, sd = 0.5)
# Check if true values fall within the prediction intervals
coverage <- mean(true_y >= predictions[, "lwr"] & true_y <= predictions[, "upr"])
cat("Out-of-sample coverage rate:", coverage)
## Out-of-sample coverage rate: 0.98
Results
- The prediction intervals are calculated using the split conformal method.
- The out-of-sample coverage rate is displayed, which should be close to the specified confidence level (e.g., 0.95).
Example: Conformal Prediction with the MASS::Boston
Dataset
In this example, we use the MASS::Boston
dataset to demonstrate conformal prediction.
Load the Data
We will use the MASS
package to access the Boston
dataset.
library(MASS)
# Load the Boston dataset
data(Boston)
# Inspect the dataset
head(Boston)
## crim zn indus chas nox rm age dis rad tax ptratio black lstat
## 1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
## 2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
## 3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
## 4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
## 5 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
## 6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21
## medv
## 1 24.0
## 2 21.6
## 3 34.7
## 4 33.4
## 5 36.2
## 6 28.7
Split the Data
We will split the data into training and test sets to ensure they are disjoint.
set.seed(123)
n <- nrow(Boston)
train_indices <- sample(seq_len(n), size = floor(0.8 * n))
train_data <- Boston[train_indices, ]
test_data <- Boston[-train_indices, ]
Fit Conformal Model 1
# Define fit and predict functions
fit_func <- function(formula, data, ...) MASS::rlm(formula, data = data, ...)
predict_func <- function(fit, newdata, ...) predict(fit, newdata, ...)
# Apply conformalize using the training data
conformal_model_boston <- misc::conformalize(
formula = medv ~ .,
data = train_data,
fit_func = fit_func,
predict_func = predict_func,
seed = 123
)
Generate Predictions and Prediction Intervals 1
We will use the predict.conformalize
method to generate predictions and calculate prediction intervals for the test set.
# Predict with split conformal method on the test data
predictions_boston <- predict(
conformal_model_boston,
newdata = test_data,
level = 0.95,
method = "split"
)
head(predictions_boston)
## fit lwr upr
## 1 29.92942 20.263283 39.59556
## 15 19.30837 9.642229 28.97451
## 17 20.71124 11.045100 30.37738
## 19 14.86650 5.200365 24.53264
## 28 14.79883 5.132688 24.46497
## 37 20.98752 11.321382 30.65366
Calculate Out-of-Sample Coverage Rate 1
The coverage rate is the proportion of true values in the test set that fall within the prediction intervals.
# True values for the test set
true_y_boston <- test_data$medv
# Check if true values fall within the prediction intervals
coverage_boston <- mean(true_y_boston >= predictions_boston[, "lwr"] & true_y_boston <= predictions_boston[, "upr"])
cat("Out-of-sample coverage rate for Boston dataset:", coverage_boston)
## Out-of-sample coverage rate for Boston dataset: 0.9509804
Fit Conformal Model 2
# Define fit and predict functions
fit_func <- function(formula, data, ...) stats::glm(formula, data = data, ...)
predict_func <- function(fit, newdata, ...) predict(fit, newdata, ...)
# Apply conformalize using the training data
conformal_model_boston <- misc::conformalize(
formula = medv ~ .,
data = train_data,
fit_func = fit_func,
predict_func = predict_func,
seed = 123
)
Generate Predictions and Prediction Intervals 2
We will use the predict.conformalize
method to generate predictions and calculate prediction intervals for the test set.
# Predict with split conformal method on the test data
predictions_boston <- predict(
conformal_model_boston,
newdata = test_data,
level = 0.95,
method = "split"
)
head(predictions_boston)
# Predict with split conformal method on the test data
predictions_boston2 <- predict(
conformal_model_boston,
newdata = test_data,
level = 0.95,
method = "kde"
)
head(predictions_boston2)
# Predict with split conformal method on the test data
predictions_boston3 <- predict(
conformal_model_boston,
newdata = test_data,
level = 0.95,
method = "surrogate"
)
head(predictions_boston3)
# Predict with split conformal method on the test data
predictions_boston4 <- predict(
conformal_model_boston,
newdata = test_data,
level = 0.95,
method = "bootstrap"
)
head(predictions_boston4)
Fit Conformal Model 2
# Define fit and predict functions
fit_func <- function(formula, data, ...) ranger::ranger(formula, data = data)
predict_func <- function(fit, newdata, ...) predict(fit, newdata)$predictions
# Apply conformalize using the training data
conformal_model_boston_rf <- misc::conformalize(
formula = medv ~ .,
data = train_data,
fit_func = fit_func,
predict_func = predict_func,
seed = 123
)
# Predict with split conformal method on the test data
predictions_boston_rf <- predict(
conformal_model_boston_rf,
newdata = test_data,
predict_func = predict_func,
level = 0.95,
method = "kde"
)
head(predictions_boston_rf)
## fit lwr upr
## [1,] 27.03134 21.991838 32.43038
## [2,] 19.20299 13.542260 25.05314
## [3,] 21.34472 17.000993 30.77696
## [4,] 18.77455 12.341589 25.88818
## [5,] 15.60764 9.157478 21.48264
## [6,] 21.31355 14.591954 29.75374
# Create a data frame for plotting
plot_data <- data.frame(
Observation = seq_len(nrow(test_data)),
TrueValue = test_data$medv,
LowerBound = predictions_boston_rf[, "lwr"],
UpperBound = predictions_boston_rf[, "upr"]
)
# Sort data by observation for proper plotting
plot_data <- plot_data[order(plot_data$Observation), ]
# Plot the true values
plot(
plot_data$Observation, plot_data$TrueValue,
pch = 16, col = "blue", cex = 0.7,
xlab = "Observation", ylab = "Value",
main = "Prediction Intervals vs True Values"
)
# Add the prediction intervals using polygon
polygon(
c(plot_data$Observation, rev(plot_data$Observation)),
c(plot_data$LowerBound, rev(plot_data$UpperBound)),
col = rgb(1, 0, 0, 0.2), border = NA
)
# Add points for true values again to overlay on the polygon
points(
plot_data$Observation, plot_data$TrueValue,
pch = 16, col = "blue", cex = 0.7
)
Calculate Out-of-Sample Coverage Rate 2
The coverage rate is the proportion of true values in the test set that fall within the prediction intervals.
# True values for the test set
true_y_boston <- test_data$medv
# Check if true values fall within the prediction intervals
coverage_boston <- mean(true_y_boston >= predictions_boston[, "lwr"] & true_y_boston <= predictions_boston[, "upr"])
cat("Out-of-sample coverage rate for Boston dataset:", coverage_boston)
## Out-of-sample coverage rate for Boston dataset: 0.9411765
# True values for the test set
true_y_boston <- test_data$medv
# Check if true values fall within the prediction intervals
coverage_boston <- mean(true_y_boston >= predictions_boston2[, "lwr"] & true_y_boston <= predictions_boston2[, "upr"])
cat("Out-of-sample coverage rate for Boston dataset:", coverage_boston)
## Out-of-sample coverage rate for Boston dataset: 0.9607843
# True values for the test set
true_y_boston <- test_data$medv
# Check if true values fall within the prediction intervals
coverage_boston <- mean(true_y_boston >= predictions_boston3[, "lwr"] & true_y_boston <= predictions_boston3[, "upr"])
cat("Out-of-sample coverage rate for Boston dataset:", coverage_boston)
## Out-of-sample coverage rate for Boston dataset: 0.9705882
# True values for the test set
true_y_boston <- test_data$medv
# Check if true values fall within the prediction intervals
coverage_boston <- mean(true_y_boston >= predictions_boston4[, "lwr"] & true_y_boston <= predictions_boston4[, "upr"])
cat("Out-of-sample coverage rate for Boston dataset:", coverage_boston)
## Out-of-sample coverage rate for Boston dataset: 0.9607843
# True values for the test set
true_y_boston <- test_data$medv
# Check if true values fall within the prediction intervals
coverage_boston <- mean(true_y_boston >= predictions_boston_rf[, "lwr"] & true_y_boston <= predictions_boston_rf[, "upr"])
cat("Out-of-sample coverage rate for Boston dataset:", coverage_boston)
## Out-of-sample coverage rate for Boston dataset: 0.9215686
Results
- The prediction intervals are calculated using the split conformal method.
- The out-of-sample coverage rate is displayed, which should be close to the specified confidence level (e.g., 0.95).
For attribution, please cite this work as:
T. Moudiki (2025-03-25). Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize. Retrieved from https://thierrymoudiki.github.io/blog/2025/03/25/r/conformalize-R
BibTeX citation (remove empty spaces)@misc{ tmoudiki20250325, author = { T. Moudiki }, title = { Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize }, url = { https://thierrymoudiki.github.io/blog/2025/03/25/r/conformalize-R }, year = { 2025 } }
Previous publications
- external regressors in ahead::dynrmf's interface for Machine learning forecasting Sep 1, 2025
- Another interesting decision, now for 'Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation' Aug 20, 2025
- Boosting any randomized based learner for regression, classification and univariate/multivariate time series forcasting Jul 26, 2025
- New nnetsauce version with CustomBackPropRegressor (CustomRegressor with Backpropagation) and ElasticNet2Regressor (Ridge2 with ElasticNet regularization) Jul 15, 2025
- mlsauce (home to a model-agnostic gradient boosting algorithm) can now be installed from PyPI. Jul 10, 2025
- A user-friendly graphical interface to techtonique dot net's API (will eventually contain graphics). Jul 8, 2025
- Calling =TECHTO_MLCLASSIFICATION for Machine Learning supervised CLASSIFICATION in Excel is just a matter of copying and pasting Jul 7, 2025
- Calling =TECHTO_MLREGRESSION for Machine Learning supervised regression in Excel is just a matter of copying and pasting Jul 6, 2025
- Calling =TECHTO_RESERVING and =TECHTO_MLRESERVING for claims triangle reserving in Excel is just a matter of copying and pasting Jul 5, 2025
- Calling =TECHTO_SURVIVAL for Survival Analysis in Excel is just a matter of copying and pasting Jul 4, 2025
- Calling =TECHTO_SIMULATION for Stochastic Simulation in Excel is just a matter of copying and pasting Jul 3, 2025
- Calling =TECHTO_FORECAST for forecasting in Excel is just a matter of copying and pasting Jul 2, 2025
- Random Vector Functional Link (RVFL) artificial neural network with 2 regularization parameters successfully used for forecasting/synthetic simulation in professional settings: Extensions (including Bayesian) Jul 1, 2025
- R version of 'Backpropagating quasi-randomized neural networks' Jun 24, 2025
- Backpropagating quasi-randomized neural networks Jun 23, 2025
- Beyond ARMA-GARCH: leveraging any statistical model for volatility forecasting Jun 21, 2025
- Stacked generalization (Machine Learning model stacking) + conformal prediction for forecasting with ahead::mlf Jun 18, 2025
- An Overfitting dilemma: XGBoost Default Hyperparameters vs GenericBooster + LinearRegression Default Hyperparameters Jun 14, 2025
- Programming language-agnostic reserving using RidgeCV, LightGBM, XGBoost, and ExtraTrees Machine Learning models Jun 13, 2025
- Exceptionally, and on a more personal note (otherwise I may get buried alive)... Jun 10, 2025
- Free R, Python and SQL editors in techtonique dot net Jun 9, 2025
- Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation Jun 7, 2025
- scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce in probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 6, 2025
- R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 5, 2025
- Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress) Jun 4, 2025
- Python version of Beyond ARMA-GARCH: leveraging model-agnostic Quasi-Randomized networks and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 3, 2025
- Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH) Jun 2, 2025
- Permutations and SHAPley values for feature importance in techtonique dot net's API (with R + Python + the command line) Jun 1, 2025
- Which patient is going to survive longer? Another guide to using techtonique dot net's API (with R + Python + the command line) for survival analysis May 31, 2025
- A Guide to Using techtonique.net's API and rush for simulating and plotting Stochastic Scenarios May 30, 2025
- Simulating Stochastic Scenarios with Diffusion Models: A Guide to Using techtonique.net's API for the purpose May 29, 2025
- Will my apartment in 5th avenue be overpriced or not? Harnessing the power of www.techtonique.net (+ xgboost, lightgbm, catboost) to find out May 28, 2025
- How long must I wait until something happens: A Comprehensive Guide to Survival Analysis via an API May 27, 2025
- Harnessing the Power of techtonique.net: A Comprehensive Guide to Machine Learning Classification via an API May 26, 2025
- Quantile regression with any regressor -- Examples with RandomForestRegressor, RidgeCV, KNeighborsRegressor May 20, 2025
- Survival stacking: survival analysis translated as supervised classification in R and Python May 5, 2025
- 'Bayesian' optimization of hyperparameters in a R machine learning model using the bayesianrvfl package Apr 25, 2025
- A lightweight interface to scikit-learn in R: Bayesian and Conformal prediction Apr 21, 2025
- A lightweight interface to scikit-learn in R Pt.2: probabilistic time series forecasting in conjunction with ahead::dynrmf Apr 20, 2025
- Extending the Theta forecasting method to GLMs, GAMs, GLMBOOST and attention: benchmarking on Tourism, M1, M3 and M4 competition data sets (28000 series) Apr 14, 2025
- Extending the Theta forecasting method to GLMs and attention Apr 8, 2025
- Nonlinear conformalized Generalized Linear Models (GLMs) with R package 'rvfl' (and other models) Mar 31, 2025
- Probabilistic Time Series Forecasting (predictive simulations) in Microsoft Excel using Python, xlwings lite and www.techtonique.net Mar 28, 2025
- Conformalize (improved prediction intervals and simulations) any R Machine Learning model with misc::conformalize Mar 25, 2025
- My poster for the 18th FINANCIAL RISKS INTERNATIONAL FORUM by Institut Louis Bachelier/Fondation du Risque/Europlace Institute of Finance Mar 19, 2025
- Interpretable probabilistic kernel ridge regression using Matérn 3/2 kernels Mar 16, 2025
- (News from) Probabilistic Forecasting of univariate and multivariate Time Series using Quasi-Randomized Neural Networks (Ridge2) and Conformal Prediction Mar 9, 2025
- Word-Online: re-creating Karpathy's char-RNN (with supervised linear online learning of word embeddings) for text completion Mar 8, 2025
- CRAN-like repository for most recent releases of Techtonique's R packages Mar 2, 2025
- Presenting 'Online Probabilistic Estimation of Carbon Beta and Carbon Shapley Values for Financial and Climate Risk' at Institut Louis Bachelier Feb 27, 2025
- Web app with DeepSeek R1 and Hugging Face API for chatting Feb 23, 2025
- tisthemachinelearner: A Lightweight interface to scikit-learn with 2 classes, Classifier and Regressor (in Python and R) Feb 17, 2025
- R version of survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, xgboost, lightgbm (and conformal prediction) Feb 12, 2025
- Model-agnostic global Survival Prediction of Patients with Myeloid Leukemia in QRT/Gustave Roussy Challenge (challengedata.ens.fr): Python's survivalist Quickstart Feb 10, 2025
- A simple test of the martingale hypothesis in esgtoolkit Feb 3, 2025
- Command Line Interface (CLI) for techtonique.net's API Jan 31, 2025
- Gradient-Boosting and Boostrap aggregating anything (alert: high performance): Part5, easier install and Rust backend Jan 27, 2025
- Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Part2: 1311 time series from the Tourism competition Jan 20, 2025
- Techtonique is out! (with a tutorial in various programming languages and formats) Jan 14, 2025
- Univariate and Multivariate Probabilistic Forecasting with nnetsauce and TabPFN Jan 14, 2025
- Just got a paper on conformal prediction REJECTED by International Journal of Forecasting despite evidence on 30,000 time series (and more). What's going on? Jan 5, 2025
- Python and Interactive dashboard version of Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 31, 2024
- Stock price forecasting with Deep Learning: throwing power at the problem (and why it won't make you rich) Dec 29, 2024
- No-code Machine Learning Cross-validation and Interpretability in techtonique.net Dec 23, 2024
- survivalist: Probabilistic model-agnostic survival analysis using scikit-learn, glmnet, xgboost, lightgbm, pytorch, keras, nnetsauce and mlsauce Dec 15, 2024
- Model-agnostic 'Bayesian' optimization (for hyperparameter tuning) using conformalized surrogates in GPopt Dec 9, 2024
- You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Pt.2: Generic Gradient Boosting Dec 1, 2024
- You can beat Forecasting LLMs (Large Language Models a.k.a foundation models) with nnetsauce.MTS Nov 24, 2024
- Unified interface and conformal prediction (calibrated prediction intervals) for R package forecast (and 'affiliates') Nov 23, 2024
- GLMNet in Python: Generalized Linear Models Nov 18, 2024
- Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
- Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
- Chat with your tabular data in www.techtonique.net Oct 30, 2024
- Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
- R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
- R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
- Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
- Gradient-Boosting anything (alert: high performance) Oct 6, 2024
- Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
- Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
- Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
- Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
- Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
- Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
- New versions of nnetsauce and ahead Sep 9, 2024
- Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
- Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
- R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
- A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
- Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
- Bayesian (nonlinear) adaptive learning Aug 12, 2024
- Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
- Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
- Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
- learningmachine for Python (new version) Jul 15, 2024
- learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
- My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
- 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
- Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
- Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
- Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
- Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
- Forecasting the Economy May 27, 2024
- A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
- Probability of receiving a loan; using learningmachine May 12, 2024
- mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
- mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
- mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
- mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
- Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
- learningmachine v1.1.2: for Python Apr 1, 2024
- learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
- Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
- Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
- rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
- ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
- A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
- nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
- Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
- Tuning Machine Learning models with GPopt's new version Jan 29, 2024
- Subsampling continuous and discrete response variables Jan 22, 2024
- DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
- A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
- learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
- A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction Dec 25, 2023
- Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
- Diffusion models in Python with esgtoolkit Dec 11, 2023
- Julia packaging at the command line Dec 4, 2023
- Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
- A plethora of datasets at your fingertips Nov 20, 2023
- A classifier that's very accurate (and deep) Nov 12, 2023
- mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
- AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
- AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
- Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
- A diffusion model: G2++ Oct 9, 2023
- Diffusion models in ESGtoolkit + announcements Oct 2, 2023
- An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
- (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
- Forecasting in Python with ahead Sep 11, 2023
- Risk-neutralize simulations Sep 4, 2023
- Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
- Reminder Apr 30, 2023
- Did you ask ChatGPT about who you are? Apr 16, 2023
- A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
- Simple interfaces to the forecasting API Nov 23, 2022
- A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
- Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
- Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
- Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
- A Machine Learning workflow using Techtonique Jun 6, 2022
- Super Mario Bros © in the browser using PyScript May 8, 2022
- News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
- Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
- New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
- A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
- Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
- Forecasting with `ahead` (Python version) Dec 13, 2021
- Tuning and interpreting LSBoost Nov 15, 2021
- Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
- Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
- Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
- Forecasting with `ahead` Oct 15, 2021
- Classification using linear regression Sep 26, 2021
- `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
- parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
- `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
- Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
- Hyperparameters tuning with GPopt Jun 11, 2021
- A forecasting tool (API) with examples in curl, R, Python May 28, 2021
- Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
- Bayesian Optimization with GPopt Apr 16, 2021
- Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
- Explaining xgboost predictions with the teller Mar 12, 2021
- An infinity of time series models in nnetsauce Mar 6, 2021
- New activation functions in mlsauce's LSBoost Feb 12, 2021
- 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
- A deeper learning architecture in nnetsauce Dec 18, 2020
- Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
- Bayesian forecasting for uni/multivariate time series Dec 4, 2020
- Generalized nonlinear models in nnetsauce Nov 28, 2020
- Boosting nonlinear penalized least squares Nov 21, 2020
- Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
- NEWS Oct 30, 2020
- A glimpse into my PhD journey Oct 23, 2020
- Submitting R package to CRAN Oct 16, 2020
- Simulation of dependent variables in ESGtoolkit Oct 9, 2020
- Forecasting lung disease progression Oct 2, 2020
- New nnetsauce Sep 25, 2020
- Technical documentation Sep 18, 2020
- A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
- Back next week, and a few announcements Sep 4, 2020
- Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
- LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
- nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
- Maximizing your tip as a waiter (Part 2) Jul 10, 2020
- New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
- Announcements Jun 26, 2020
- Parallel AdaOpt classification Jun 19, 2020
- Comments section and other news Jun 12, 2020
- Maximizing your tip as a waiter Jun 5, 2020
- AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
- AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
- AdaOpt May 15, 2020
- Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
- Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
- Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
- Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
- Grid search cross-validation using crossval Apr 10, 2020
- Documentation for the querier, a query language for Data Frames Apr 3, 2020
- Time series cross-validation using crossval Mar 27, 2020
- On model specification, identification, degrees of freedom and regularization Mar 20, 2020
- Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
- R notebooks for nnetsauce Mar 6, 2020
- Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
- Create a specific feed in your Jekyll blog Feb 21, 2020
- Git/Github for contributing to package development Feb 14, 2020
- Feedback forms for contributing Feb 7, 2020
- nnetsauce for R Jan 31, 2020
- A new version of nnetsauce (v0.3.1) Jan 24, 2020
- ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
- Search bar, new year 2020 Jan 10, 2020
- 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
- Understanding model interactions with the `teller` Dec 13, 2019
- Using the `teller` on a classifier Dec 6, 2019
- Benchmarking the querier's verbs Nov 29, 2019
- Composing the querier's verbs for data wrangling Nov 22, 2019
- Comparing and explaining model predictions with the teller Nov 15, 2019
- Tests for the significance of marginal effects in the teller Nov 8, 2019
- Introducing the teller Nov 1, 2019
- Introducing the querier Oct 25, 2019
- Prediction intervals for nnetsauce models Oct 18, 2019
- Using R in Python for statistical learning/data science Oct 11, 2019
- Model calibration with `crossval` Oct 4, 2019
- Bagging in the nnetsauce Sep 25, 2019
- Adaboost learning with nnetsauce Sep 18, 2019
- Change in blog's presentation Sep 4, 2019
- nnetsauce on Pypi Jun 5, 2019
- More nnetsauce (examples of use) May 9, 2019
- nnetsauce Mar 13, 2019
- crossval Mar 13, 2019
- test Mar 10, 2019
Comments powered by Talkyard.