measures.precision_at_k(Y_true, Y_pred[, k]) |
Calculate precision at 1-k places. |
measures.recall_at_k(Y_true, Y_pred[, k, …]) |
Calculate recall at 1-k places. |
measures.coverage_at_k(Y_true, Y_pred[, k]) |
Calculate coverage at 1-k places. |
measures.dcg_at_k(Y_true, Y_pred[, k]) |
Calculate Discounted Cumulative Gain (DCG) at 1-k places. |
measures.Jain_et_al_inverse_propensity(Y[, A, B]) |
Calculate inverse propensity as proposed in Jain et al. |
measures.Jain_et_al_propensity(Y[, A, B]) |
Calculate propensity as proposed in Jain et al. |
measures.ndcg_at_k(Y_true, Y_pred[, k, …]) |
Calculate normalized Discounted Cumulative Gain (nDCG) at 1-k places. |
measures.psprecision_at_k(Y_true, Y_pred, inv_ps) |
Calculate Propensity Scored Precision (PSP) at 1-k places. |
measures.psrecall_at_k(Y_true, Y_pred, inv_ps) |
Calculate Propensity Scored Recall (PSR) at 1-k places. |
measures.psdcg_at_k(Y_true, Y_pred, inv_ps) |
Calculate Propensity Scored Discounted Cumulative Gain (PSDCG) at 1-k places. |
measures.psndcg_at_k(Y_true, Y_pred, inv_ps) |
Calculate Propensity Scored normalized Discounted Cumulative Gain (PSnDCG) at 1-k places. |
measures.hamming_loss(Y_true, Y_pred) |
Calculate unnormalized (to avoid very small numbers because of large number of labels) hamming loss - average number of misclassified labels. |
measures.f1_measure(Y_true, Y_pred[, …]) |
Calculate F1 measure, also known as balanced F-score or F-measure. |