Sourcetable brings machine learning into the spreadsheet. No data science experience required — describe what you want to predict or classify, and the AI builds and runs the model for you.Documentation Index
Fetch the complete documentation index at: https://sourcetable.com/docs/llms.txt
Use this file to discover all available pages before exploring further.
TabPFN (in-context learning)
TabPFN is a neural network that can make predictions on tabular data without training. It works directly on your spreadsheet data:Predict mode
Forecast numerical values based on patterns in your data:- “Predict next month’s revenue based on the last 24 months”
- “Estimate the sale price for these properties based on size, location, and age”
- “Forecast customer lifetime value based on their first 90 days of activity”
Classify mode
Categorize rows based on features in your data:- “Classify these leads as hot, warm, or cold based on their engagement metrics”
- “Predict which customers are likely to churn based on usage patterns”
- “Categorize these support tickets by priority level”
scikit-learn
For more traditional machine learning workflows, the AI uses scikit-learn under the hood:Clustering
- “Group these customers into 5 segments based on their purchase behavior”
- “Find natural clusters in this dataset”
Regression
- “Build a linear regression model predicting house prices”
- “What factors most influence customer satisfaction scores?”
Classification
- “Train a model to classify emails as spam or not spam”
- “Predict which deals will close based on historical win/loss data”
Dimensionality reduction
- “Reduce these 50 features to the most important 5”
- “Create a 2D visualization of these high-dimensional data points”
How the AI uses ML
When you ask for a prediction or classification, the AI:- Reads your data from the spreadsheet
- Selects features — determines which columns are inputs vs. targets
- Handles preprocessing — missing values, encoding, scaling
- Trains the model (or uses TabPFN for zero-shot prediction)
- Returns results — predictions, confidence scores, feature importance
- Writes back to your spreadsheet
Available libraries
| Library | Capabilities |
|---|---|
| TabPFN | Zero-shot prediction and classification for tabular data |
| scikit-learn | Classification, regression, clustering, dimensionality reduction |
| SciPy | Statistical tests, distributions, optimization |
| StatsModels | Regression analysis, time series, hypothesis testing |
| NumPy | Numerical computing, linear algebra |
| pandas | Data manipulation and preprocessing |
Use cases
| Scenario | Approach |
|---|---|
| Sales forecasting | Predict mode with historical sales data |
| Churn prediction | Classify mode with customer activity data |
| Customer segmentation | scikit-learn clustering |
| Price optimization | Regression with feature importance |
| Anomaly detection | Classify or cluster with outlier identification |
| A/B test analysis | StatsModels hypothesis testing |
| Demand forecasting | Time series with StatsModels |