Getting started¶
This page walks you from "I have no formulaML add-in installed" to "I trained my first model and used it to predict new values" — entirely in Excel cells.
1. Install the add-in¶
Visit formulaml.com and follow the install button.
Excel's add-in pane will load the formulaML task pane on the right side of
your spreadsheet. After installation, the ML.* namespace becomes available
in any cell.
To confirm the add-in is connected, type =ML. in a cell. Excel's formula
autocomplete should suggest ML.CLASSIFICATION, ML.REGRESSION,
ML.CLUSTERING, and the other top-level namespaces.
2. Load a sample dataset¶
formulaML ships with built-in datasets you can call directly. Pick any empty cell and enter:
The cell now displays an object handle — a
small icon next to a label like Dataset. The full dataset lives on the
formulaML server; the cell holds a pointer to it.
3. Split the data into features and target¶
Most models expect two ranges: a matrix of features (X) and a vector of
labels (y). Use the dataset helpers to extract them. Suppose the dataset
handle from step 2 is in A1:
Spill the results into adjacent ranges, e.g. B1:E150 for X and F1:F150
for y.
4. Train your first model¶
Use a logistic regression classifier — fast, interpretable, and a sensible baseline:
ML.CLASSIFICATION.LOGISTIC() returns an unfitted model handle in H1.
ML.FIT(...) consumes that handle and the data, and returns a fitted
model handle.
5. Predict on new data¶
Pass the fitted-model handle plus a new feature range to ML.PREDICT:
The result spills as a vector of predicted labels.
What's next¶
- Learn the FIT/PREDICT pattern used by every model in formulaML.
- Browse the Reference to see every available function and its arguments.
- Replace the logistic regression with
ML.CLASSIFICATION.RANDOM_FOREST_CLForML.REGRESSION.RIDGEand re-run the FIT/PREDICT chain — every model follows the same workflow.