Feature Dependence in Learning
Feature dependence describes how two inputs move together. It can be linear, ranked, grouped, or nonlinear. Strong dependence is not always bad. It can show a useful signal. Yet it can also create duplicate information. Duplicate signals may slow training. They may also make explanations unstable.
Why Dependence Matters
Machine learning models use patterns. When two columns carry the same pattern, the model may overvalue that pattern. Linear models can show inflated coefficients. Tree models may split on either feature without a clear reason. Distance models may give repeated weight to one concept. Checking dependence helps you reduce this risk before training.
Reading the Main Scores
Pearson correlation measures straight line movement. Spearman correlation measures monotonic rank movement. Mutual information can detect wider relationships. Cramer’s V helps when both features are categorical. Correlation ratio helps when one feature is categorical and the other is numeric. No single score explains everything. Use the graph and table beside the scores.
Choosing a Practical Cutoff
A value near zero usually means weak dependence. A value near one means strong dependence. Many teams review pairs above 0.70. Pairs above 0.90 often need action. The right cutoff depends on domain value, model type, and sample size. A rare but important feature may stay even if it is dependent.
Next Steps After Analysis
When dependence is high, compare business meaning first. Keep the feature that is cleaner, cheaper, or easier to explain. You can also combine features into a ratio, score, or index. For linear models, variance inflation can guide removal. For nonlinear models, test performance with and without the feature. Always validate changes with a holdout set. This keeps decisions tied to model quality, not just statistics.
Using This Page
Paste paired values in the two boxes. Select the best data type. Increase bins when numeric values have many ranges. Press calculate. Review the summary, plot, and download files. Save the report for feature selection notes. Repeat this process for important feature pairs.
Document assumptions clearly. Note missing values. Record bin choices. Recheck dependence after encoding, scaling, or feature engineering. New transformations can change relationships quickly again later too.