Regression model trainer
Paste a numeric table, choose a target column, and train a deterministic regression model locally with metrics, coefficients or feature importance.
About this tool
Train a regression model from a pasted numeric table without uploading data. The tool parses CSV, TSV, semicolon, pipe, or whitespace-delimited rows, picks a target column, and fits ordinary least squares, ridge regression, or a bounded deterministic random forest. Reports include R², RMSE, MAE, row counts, optional hold-out and cross-validation metrics, plus coefficients for linear/ridge or feature-importance shares for random forest.
Worked example:
x,y
1,3
2,5
3,7
4,9
With target y and model linear, the fitted equation is y = 1 + 2·x, with R² of 1 and RMSE 0.
For multiple predictors, set features to a comma-separated list such as sqft,rooms, or leave it
blank to use every non-target numeric column.
Limits and edge cases: the table is capped at 20,000 rows and 100 columns. All selected predictors and the target must be numeric; encode categories as numeric dummy columns before using this tool. Random forest work is capped, so reduce rows, trees, or CV folds for larger tables. Coefficients are not causal claims; they summarize the pasted data under the chosen model.
FAQ
Which model should I choose?
Use linear for a quick ordinary least squares equation, ridge when predictors are correlated or
have different scales, and random_forest when the relationship is nonlinear and you care more about
prediction and importance than a simple equation.
How do I select columns?
target accepts last, first, a 1-based index, or a header name. features is optional; leave it
blank to use every other column, or list columns such as sqft,rooms or 1,3 to fit only those
predictors.
What happens to missing or non-numeric values?
Rows with missing values such as NA, null, or blank cells in selected columns are dropped and
reported. Non-numeric text in selected predictors or the target is an error, because this tool does
not perform automatic categorical encoding.
Is the random forest reproducible?
Yes. Bootstrap samples, feature choices, train/test splits, and cross-validation folds all use the
seed value, so the same data and options produce the same report.
Developer & Automation Access
Run it from the terminal
Same engine as this page, headless — via the gizza CLI:
gizza tool regression-model-trainer "x,y
1,3
2,5
3,7
4,9"New to the CLI? Get gizza →
Open it by URL
Pre-fill and auto-run this tool with query parameters — the names match the API/CLI:
https://gizza.ai/tools/regression-model-trainer/?data=x%2Cy%0A1%2C3%0A2%2C5%0A3%2C7%0A4%2C9&target=last&features=x1%2Cx2&model=linear&alpha=1&standardize=true&trees=100&max_depth=8&test_split=0&cv_folds=0&seed=42&header=auto&decimals=4&format=textMachine-readable descriptor: tool.json — title + parameters JSON Schema for agents.