Discriminant analysis (LDA/QDA)
Multivariate analysis · Method and input reference
Numeric complete observations. PCA standardizes variables; EFA uses maximum likelihood without rotation. CFA accepts measurement lines only. Discriminant analysis uses first column as class; reports leave-one-out accuracy. Canonical correlation splits columns into two sets. Synthetic example: 180 people: group code (0–2), then three overlapping health measures; classification is deliberately imperfect.
Open calculator · Compare methods in the decision matrix
When to consider it
- Classify known groups from numeric predictors with cross-validation.
Assumptions and scope
- Observations must be independent between subjects; repeated rows from one subject are not independent samples.
- Use the outcome and predictor coding required by the calculator; review missing data and model diagnostics.
Required inputs
- Data matrix (one observation per line; comma-separated columns) — Numeric observations; preserve the row and column structure described above.
- Method — Choose LDA, QDA.
Review missing values and data coding before running. Sample inputs are synthetic teaching examples.
Implementation
Lifescio validates inputs and formats results; listed functions are the principal calculation routines, not an exhaustive transitive dependency list.
- scikit-learn 1.7.2: sklearn.discriminant_analysis.LinearDiscriminantAnalysis, sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis, sklearn.model_selection.cross_val_predict
These are pinned library versions; saved results record the versions actually used at runtime.