diff --git a/verticapy/tests_new/machine_learning/metrics/test_classification_metrics.py b/verticapy/tests_new/machine_learning/metrics/test_classification_metrics.py
index 5e5ffa8f3..46a5d0015 100644
--- a/verticapy/tests_new/machine_learning/metrics/test_classification_metrics.py
+++ b/verticapy/tests_new/machine_learning/metrics/test_classification_metrics.py
@@ -252,6 +252,7 @@ def python_metrics(y_true, y_pred, average="binary", metric_name=""):
         ("n", "negative_likelihood_ratio"),
         ("n", "positive_likelihood_ratio"),
         ("y", "precision_score"),
+        ("y", "average_precision_score"),
         ("n", "prevalence_threshold"),
         ("y", "recall_score"),
         ("n", "specificity_score"),
@@ -321,18 +322,30 @@ def get_vertica_metrics():
                 "prc_auc_score",
                 "best_cutoff",
                 "log_loss",
+                "average_precision_score",
             ]:
                 if compute_method == "binary":
-                    _vpy_res = getattr(vpy_metrics, metric_name)(
-                        "y_true", "y_prob", vdf, average=compute_method, **func_args
-                    )
+                    if metric_name == "average_precision_score":
+                        _vpy_res = getattr(vpy_metrics, metric_name)(
+                            "y_true_num",
+                            "y_pred_num",
+                            vdf,
+                            average=compute_method,
+                            pos_label="1",
+                        )
+                    else:
+                        _vpy_res = getattr(vpy_metrics, metric_name)(
+                            "y_true", "y_prob", vdf, average=compute_method, **func_args
+                        )
                 else:
                     _vpy_res = getattr(vpy_metrics, metric_name)(
                         "y_true_num",
                         ["y_prob0", "y_prob1", "y_prob2"],
                         vdf,
                         average=compute_method,
-                        labels=labels_num,
+                        labels=[str(label_num) for label_num in labels_num]
+                        if metric_name == "average_precision_score"
+                        else labels_num,
                     )
                 # rounding as best_cutoff metrics value precisions are upto 2/3 decimals
                 _vpy_res = (
@@ -418,6 +431,18 @@ def get_python_metrics():
                             _y_true_num.ravel(), y_prob.ravel()
                         )
                         _skl_res = skl_metrics.auc(fpr, tpr)
+                elif metric_name in ["average_precision_score"]:
+                    if compute_method == "binary":
+                        _skl_res = getattr(skl_metrics, metric_name)(
+                            y_true_num, y_pred_num, pos_label=1
+                        )
+                    else:
+                        _skl_res = getattr(skl_metrics, metric_name)(
+                            y_true_num,
+                            y_prob,
+                            average=compute_method,
+                            pos_label=1,
+                        )
                 else:
                     _skl_res = getattr(skl_metrics, metric_name)(
                         y_true, y_pred, labels=labels
@@ -435,8 +460,6 @@ def get_python_metrics():
                         _skl_res = skl_metrics.auc(recall, precision)
                     else:
                         y_true_num = label_binarize(y_true, classes=[0, 1, 2])
-                        print()
-                        print(y_true_num)
                         fpr, tpr, thresholds = skl_metrics.roc_curve(
                             y_true_num, y_prob, pos_label="b"
                         )
@@ -456,6 +479,13 @@ def get_python_metrics():
 
                     # rounding as best_cutoff metrics value precisions are upto 2/3 decimals
                     _skl_res = round(_skl_res, 2)
+                elif metric_name in ["average_precision_score"]:
+                    _skl_res = getattr(skl_metrics, metric_name)(
+                        y_true_num,
+                        y_prob,
+                        average=None,
+                        pos_label=1,
+                    )
                 else:
                     _skl_res = python_metrics(
                         y_true, y_pred, average=compute_method, metric_name=metric_name