Skip to main content
`Ctrl`+`K`

# skbio.stats.ordination.MMvecResult.predict

MMvecResult.predict(X)

  Predict conditioned feature compositions given conditioning features.

  The expected conditioned (Y) feature distribution for each sample is computed by marginalizing over the given conditioning (X) feature composition and the learned conditional probabilities:

  \[P(Y) = \sum_i P(X_i)\,P(Y \mid X_i)\]
  Parameters:
    X table_like of shape (n_samples, n_features_x)

      Feature abundance table of the conditioning (X) modality. Columns must match the features used during training.

  Returns:
    table_like of shape (n_samples, n_features_y)

      Predicted feature proportions of the conditioned (Y) modality for each sample. Each row sums to 1.