fastml

GitHub - selcukorkmaz/fastml: Guarded Resampling for Safe and Automated Machine Learning in R Guarded Resampling for Safe and Automated Machine Learning in R - selcukorkmaz/fastml

fastml install options:
✅ Stable release from CRAN
🧪 Development version from GitHub

install.packages("fastml")
# or
remotes::install_github("selcukorkmaz/fastml")

github.com/selcukorkmaz...

New in fastml v0.7.7 ✅
The BreastCancer example now prints two tables:
Table 1 selects the best model via CV ROC AUC (mean±SD).
Table 2 reports full test-set metrics for all models (accuracy, F1, ROC AUC, logloss, Brier, ECE).

fastml: Guarded Resampling Workflows for Safe and Automated Machine Learning in R Provides a guarded resampling workflow for training and evaluating machine-learning models. When the guarded resampling path is used, preprocessing and model fitting are re-estimated within each resam...

fastml 0.7.7 is now on CRAN!

New: Feature Importance Stability Analysis - see which predictors are consistently important across CV folds vs. those that vary

Also: Enhanced explainability infrastructure, improved CV metrics aggregation & bug fixes

cran.r-project.org/web/packages...

fastml: Guarded Resampling Workflows for Safe and Automated Machine Learning in R Provides a guarded resampling workflow for training and evaluating machine‑learning models. When the guarded resampling path is used, preprocessing and model fitting are re‑estimated within each resam...

🚀 fastml 0.7.6 has landed on CRAN!

We've upgraded Guarded Resampling to strictly prevent data leakage, ensuring your validation scores are real, not optimistic hallucinations.

Get it now: install.packages("fastml")

#rstats #datascience

cran.r-project.org/web/packages...

GitHub - selcukorkmaz/fastml at devel Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code. - GitHub - selcukorkmaz/fastml at devel

fastml: Leakage-proof AutoML for R with stronger guarantees—guarded resampling (preprocessing re-fit per fold), native survival models (Penalized Cox, XGBoost AFT), single-call multi-model benchmarking, reproducibility capsule, and multiple built-in explainability methods.
github.com/selcukorkmaz...

GitHub - selcukorkmaz/fastml at devel Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code. - GitHub - selcukorkmaz/fastml at devel

fastml: Leakage-proof AutoML for R with stronger guarantees—guarded resampling (preprocessing re-fit per fold), native survival models (Penalized Cox, XGBoost AFT), single-call multi-model benchmarking, reproducibility capsule, and multiple built-in explainability methods.
github.com/selcukorkmaz...

GitHub - selcukorkmaz/fastml at devel Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code. - GitHub - selcukorkmaz/fastml at devel

Introducing fastml for R! 🚀
🛡️ Guarded Resampling: Prevents data leakage by design.
🌲 Native Survival: Custom XGBoost AFT & Piecewise engines that outperform baselines.
🔒 Security Sandbox: Safe execution for user-defined recipes.
Build robust models in one line of code. #rstats #automl #datascience

GitHub - selcukorkmaz/fastml at devel Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code. - GitHub - selcukorkmaz/fastml at devel

Introducing fastml for R! 🚀
🛡️ Guarded Resampling: Prevents data leakage by design.
🌲 Native Survival: Custom XGBoost AFT & Piecewise engines that outperform baselines.
🔒 Security Sandbox: Safe execution for user-defined recipes.
Build robust models in one line of code. #rstats #automl #datascience

GitHub - selcukorkmaz/fastml: Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code. Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code. - selcukorkmaz/fastml

🚀 Tired of writing 200 lines of code just to train and tune models in R? Meet fastml.

fastml takes data from raw form to tuned models with explainability in a single function call.

Here is a thread on solving Customer Churn in <10 lines of code. 🧵👇
#rstats #datascience
github.com/selcukorkmaz...

fastml bridges the gap between "AutoML" and "Rigorous Statistics."

It enforces safety guards against data leakage while giving you the speed of modern ML.

📦 Try it out development version: devtools::install_github("selcukorkmaz/fastml@devel")

5️⃣ The Solution (Counterfactuals)

"What if we offered Customer 56 a 1-year contract?"

Using fastexplain(
model_results,
method = "counterfactual",
observation = risky_customer
)

Moving from "Month-to-Month" to "One Year" drops their churn risk from ~76% to ~26%.

4️⃣ The "Why" (Local)

Let's look at Customer 56. They have a 76% probability of churning. Why?

Using

fastexplain(
model_results,
method = "breakdown",
observation = risky_customer),

we see the additive drivers: 🔴 Fiber Optic Internet (+10.8%) 🔴 Low Tenure (+13.4%)

The "Why" (Global)

We can’t trust a black box. Running fastexplain(model_results, method = "dalex") reveals the drivers across the whole company.

📉 Tenure and Contract Type are the biggest predictors of churn.

2️⃣ The Leaderboard

Who won? Surprisingly, Logistic Regression took the crown 👑 with an AUC of 0.846, beating Random Forest and XGBoost.

summary(model_results) gives you metrics, formatted and ready for reporting and plot(model_results, type = "roc") visualizes ROC curves.

1️⃣ The One-Liner

We pass the raw wa_churn dataset to fastml().

It automatically: ✅ Handles missing values (medianImpute) ✅ Encodes categoricals ✅ Splits data ✅ Runs Bayesian Optimization on XGBoost, RF, and LogReg.

No recipes. No boilerplate. Just results. ⚡️

GitHub - selcukorkmaz/fastml: Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code. Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code. - selcukorkmaz/fastml

🚀 Tired of writing 200 lines of code just to train and tune models in R? Meet fastml.

fastml takes data from raw form to tuned models with explainability in a single function call.

Here is a thread on solving Customer Churn in <10 lines of code. 🧵👇
#rstats #datascience
github.com/selcukorkmaz...

fastml brings a unified machine-learning workflow to R.

• Automated model training and tuning
• Leakage-safe resampling by design
• Built-in survival analysis
• Integrated explainability

A streamlined way to build reliable models with minimal code.

#rstats #machinelearning #datascience

fastml brings a unified machine-learning workflow to R.

• Automated model training and tuning
• Leakage-safe resampling by design
• Built-in survival analysis
• Integrated explainability

A streamlined way to build reliable models with minimal code.

#rstats #machinelearning #datascience

fastml: Fast Machine Learning Model Training and Evaluation Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code by providing comprehensive data preprocessing and support for a wide range of algorithms with...

🚀 Stop writing hundreds of lines of boilerplate code for Machine Learning in R.

If you’ve used tidymodels or mlr3, you know the workflow can become verbose: recipes, encoders, CV folds, tuning grids, leakage risks…

fastml aims to solve this.

cran.r-project.org/web/packages...