Prune highly correlated features

About this tool

Correlated Feature Pruner reduces multicollinearity in a numeric dataset by dropping redundant columns. It computes the pairwise correlation between your columns (each column is a feature, each row an observation) and greedily removes one column from every pair whose absolute correlation is stronger than the threshold you set. The first-seen column of each correlated group is kept, so the result is deterministic. Everything runs locally in your browser — nothing is uploaded.

This is the same "keep the first, drop the later" recipe used by common pandas / feature-engine multicollinearity filters, done in one paste. Highly collinear features carry the same information, inflate variance in linear models, and make coefficients unstable — pruning them before modeling keeps your feature set lean and interpretable.

Worked example

Input (with a header row):

age,income,savings
20,100,5
30,200,9
40,300,2
50,400,7

Here income moves in lockstep with age (|r| = 1.00), while savings is essentially uncorrelated. With the default threshold |r| > 0.90 and Pearson correlation:

Kept 2 of 3 columns (threshold |r| > 0.90, pearson).

Kept (2): age, savings
Dropped (1):
  income (|r|=1.00 with age)

Pruned data:
age,savings
20,5
30,9
40,2
50,7

Each dropped column reports the correlation value and the kept column it was redundant with, and the pruned dataset is emitted as CSV of the kept columns.

Options

• Threshold (|r|) — the absolute-correlation cutoff, from 0 to 1 (default 0.9). A column is dropped when its |correlation| with an already-kept column is strictly greater than this. Lower it (e.g. 0.8) to prune more aggressively; raise it (0.95) to keep more. The comparison is exclusive, so |r| = 1.0 at threshold 1.0 keeps both.
• Correlation method — Pearson (linear, default), Spearman (rank-based, catches monotonic-but-nonlinear relationships), or Kendall (tie-corrected tau-b, robust rank concordance).
• Column names — optional comma-separated labels; otherwise columns default to v1..vN (or the header row).
• First row is a header — treat the first line as column names instead of data.

Limits

• The pruning is greedy and order-dependent by design: within a correlated group the left-most column is kept. Reorder your columns (or rename via the labels field) if you want a different survivor.
• A constant column has undefined correlation; it is treated as uncorrelated, so it is never dropped and never forces a drop.
• Correlation measures pairwise redundancy, not a full multicollinearity diagnostic — three columns can be jointly collinear while no single pair trips the threshold. Use a VIF check for that case.
• Everything happens in-browser on a single paste; there is no file upload or server-side batch. Use Copy or Download for the result.

FAQ