diff --git a/quacc/main.py b/quacc/main.py
index 557e093..8fd148d 100644
--- a/quacc/main.py
+++ b/quacc/main.py
@@ -5,7 +5,6 @@ from quapy.data import LabelledCollection
 from quapy.protocol import APP, AbstractStochasticSeededProtocol
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import cross_val_predict
-from sklearn.svm import LinearSVC
 
 
 # Extended classes
diff --git a/quacc/quantifier.py b/quacc/quantifier.py
new file mode 100644
index 0000000..20bc151
--- /dev/null
+++ b/quacc/quantifier.py
@@ -0,0 +1,47 @@
+import numpy as np
+import scipy.sparse as sp
+from quapy.data import LabelledCollection
+from quapy.method.base import BaseQuantifier
+from sklearn.base import BaseEstimator
+from sklearn.model_selection import cross_val_predict
+
+
+def _get_ex_class(classes, true_class, pred_class):
+    return true_class * classes + pred_class
+
+
+def _extend_collection(coll, pred_prob):
+    n_classes = coll.n_classes
+
+    # n_X = [ X | predicted probs. ]
+    if isinstance(coll.X, sp.csr_matrix):
+        pred_prob_csr = sp.csr_matrix(pred_prob)
+        n_x = sp.hstack([coll.X, pred_prob_csr])
+    elif isinstance(coll.X, np.ndarray):
+        n_x = np.concatenate((coll.X, pred_prob), axis=1)
+    else:
+        raise ValueError("Unsupported matrix format")
+
+    # n_y = (exptected y, predicted y)
+    n_y = []
+    for i, true_class in enumerate(coll.y):
+        pred_class = pred_prob[i].argmax(axis=0)
+        n_y.append(_get_ex_class(n_classes, true_class, pred_class))
+
+    return LabelledCollection(n_x, np.asarray(n_y), [*range(0, n_classes * n_classes)])
+
+
+class AccuracyQuantifier:
+    def __init__(self, model: BaseEstimator, q_model: BaseQuantifier):
+        self.model = model
+        self.q_model = q_model
+
+    def fit(self, train: LabelledCollection):
+        self._train = train
+        self.model.fit(*self._train.Xy)
+        self._pred_prob_train = cross_val_predict(
+            self.model, *self._train.Xy, method="predict_proba"
+        )
+        self._e_train = _extend_collection(self._train, self._pred_prob_train)
+
+        self.q_model.fit(self._e_train)