Correlation Matrix Threshold Calculator

Work out correlation matrix threshold instantly with clear inputs, formula shown and shareable results.

About Correlation Matrix Threshold Calculator

A correlation matrix over p features contains p(p-1)/2 unique pairs. Pruning collinearity means dropping one member of each highly correlated pair, and because a cluster of m mutually correlated features produces m(m-1)/2 pairs, the number of features to drop is recovered by inverting that relationship. Removing collinear inputs stabilises linear coefficients and shrinks tree models without losing signal.

Formula

Pair count and pruning

pairs = p(p-1)/2; features implicated by k pairs ~ ceil((-1 + sqrt(1 + 8k)) / 2); drop all but one per cluster

Worked example

With the default values below, Correlation Matrix Threshold Calculator returns Features to remove for de-correlation of 7. Change any input above and every figure here is recalculated in your browser.

Inputs used

Number of featuresminimum 2: 30
Absolute correlation thresholdaccepts 0.1–0.99: 0.85
Pairs above the thresholdminimum 0: 24

Results

Features to remove for de-correlation: 7
Unique feature pairs examined: 435
Share of pairs above the threshold: 5.52%
Features retained: 23

Step by step

1Values used
2Pair count and pruning
3Features to remove for de-correlation= 7
4Unique feature pairs examined= 435
5Share of pairs above the threshold= 5.52
6Features retained= 23

Assumptions and limits

The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Does collinearity hurt tree models?

It rarely hurts accuracy but it splits importance between duplicated features, which makes explanations misleading and feature selection unstable.

Which feature of a correlated pair should I keep?

Keep the one that is cheaper to collect, more stable over time, or more interpretable. If both are equal, keep the one with higher correlation to the target.