Stepwise feature selection for regression

About this tool

Stepwise feature selection helps you choose a smaller ordinary-least-squares regression model from a wider numeric table. Paste one observation per row, choose the target column, and select whether the search should start empty and add terms, start full and remove terms, or move in both directions. The output is a plain-text audit trail: every accepted add/drop move, the selected predictors, coefficient estimates, standard errors, t and p values, R² / adjusted R², RMSE, information criteria, an F-test, dropped predictors, and a null-vs-selected-vs-full model comparison.

Use it for quick exploratory screening before a deeper model review. It is deterministic, local, and works with comma-, tab-, semicolon-, or whitespace-separated numeric rows. The first row can be a header, or you can supply labels separately.

Worked example

Paste this table and set Target column to sales, First row is a header on, and Search direction to Both:

ads,price,temp,sales
2,19,21,30.4
5,15,14,49.0
3,18,30,37.0
8,12,18,67.1
6,14,25,56.4
9,11,11,75.1
4,17,27,40.4
7,13,16,62.7
10,10,23,79.7
5,16,29,48.4
8,12,13,67.8
3,20,19,31.9

The report should keep the advertising and price columns, drop the temperature noise column, and show the final fitted equation with the selected model's fit statistics.

Limits and edge cases

• The tool accepts up to 20,000 rows and 60 total columns, including the target.
• All values after an optional header must be finite numbers; missing cells and text cells are reported with their row and column.
• Stepwise selection is a screening heuristic, not a guarantee of causal predictors or stable inference. Validate the chosen model on held-out data and with domain knowledge.
• Perfectly collinear candidate subsets are skipped so the search can continue, but a table with too few usable rows or a constant target is rejected.
• AIC, BIC, and AICc compare models on the same data. BIC penalizes larger models more strongly; pvalue mode uses alpha_enter and alpha_remove thresholds instead.

FAQ