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Sourcetable supports two ML approaches: TabPFN for instant zero-shot predictions, and scikit-learn for full model training pipelines.

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TabPFN — zero-shot predictions

TabPFN is a pre-trained neural network that makes predictions on tabular data without any training step. It works immediately on your data.

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When to use TabPFN

  • Small to medium datasets (under 10,000 rows works best)
  • Quick prototyping — get results in seconds
  • No hyperparameter tuning needed
  • Classification and regression tasks

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Available modes

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scikit-learn — full ML pipeline

For larger datasets or when you need more control, Sourcetable uses scikit-learn under the hood.

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Classification

Available algorithms:

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Regression

Available algorithms: Linear Regression, Ridge, Lasso, ElasticNet, Random Forest Regressor, Gradient Boosting Regressor, SVR, Decision Tree Regressor.

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Clustering

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Dimensionality reduction

Available methods: PCA, t-SNE, UMAP, LDA.

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End-to-end ML pipeline

When you ask the AI to build a model, it automatically handles:
  1. Data splitting — train/test split (default 80/20)
  2. Feature preprocessing — encoding categoricals, scaling numerics, handling missing values
  3. Model training — fits the chosen algorithm
  4. Evaluation — generates metrics and visualizations
  5. Results — writes predictions back to your spreadsheet

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Model evaluation

The AI reports relevant metrics based on the task: Classification metrics:
  • Accuracy, Precision, Recall, F1 Score
  • ROC curve and AUC
  • Confusion matrix
  • Classification report by class
Regression metrics:
  • R-squared and Adjusted R-squared
  • MAE (Mean Absolute Error)
  • RMSE (Root Mean Squared Error)
  • Residual plots
Clustering metrics:
  • Silhouette score
  • Calinski-Harabasz index
  • Inertia (for K-Means)

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Hyperparameter tuning

The AI performs grid search or randomized search with cross-validation to find optimal parameters.