StatSimFit Version: 0.0.2

Fit XGBoost models online

Easily fit a machine learning model and predict new values

  1. Select a training dataset in the CSV format
  2. Select a dataset for prediction
  3. Choose a target variable (variable you want to predict)
  4. Customize XGBoost model
  5. Click Run

The best model for tabular data

XGBoost is probably the best model for tabular data with independent observations (not time-series). It's based on an ensemble of decision trees and won a lot of public competitions. XGBoost supports classification, regression, count, and survival modes.

Cross-validation for small datasets

In many cases, a dataset is too small to evaluate a model performance correctly. A standard practice of splitting data on training and test sets gives unreliable results. Cross-validation helps with this problem by running multiple splits iteratively and calculating a more robust score based on those runs.

Download a model and its predictions

After a model is fit and made predictions, you can download a resulting CSV file for further usage. It includes the same input columns together with the predicted target column. You can also download the fitted model in JSON format and charts with XGBoost performance on this dataset as SVGs.

Client-side processing

XGBoost is a native C++ library ported to WebAssembly and wrapped in this web application. When you load a dataset, it's processed using a virtual machine running in your browser. That means no single byte of your data is leaving a device that runs this web app.

If you enjoyed the app, star us on GitHub. To report errors, create an issue.

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