diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
index db7b59d6c1d3a..37a720a3d130b 100644
--- a/doc/modules/model_evaluation.rst
+++ b/doc/modules/model_evaluation.rst
@@ -1001,6 +1001,34 @@ if the predicted outputs have been binarized.
     for an example of using ROC to
     model species distribution.
 
+.. _det_curve:
+
+Detection error tradeoff (DET)
+---------------------------------------
+
+The function :func:`detection_error_tradeoff` computes the
+`detection error tradeoff curve, or DET curve <https://en.wikipedia.org/wiki/Detection_error_tradeoff>`_.
+Quoting Wikipedia :
+
+  "A detection error tradeoff (DET) graph is a graphical plot of error rates for binary classification systems, plotting false reject rate vs. false accept rate. The x- and y-axes are scaled non-linearly by their standard normal deviates (or just by logarithmic transformation), yielding tradeoff curves that are more linear than ROC curves, and use most of the image area to highlight the differences of importance in the critical operating region."
+
+This function requires the true binary
+value and the target scores, which can either be probability estimates of the
+positive class, confidence values, or binary decisions.
+Here is a small example of how to use the :func:`detection_error_tradeoff` function::
+
+    >>> import numpy as np
+    >>> from sklearn.metrics import det_error_tradeoff
+    >>> y = np.array([1, 1, 2, 2])
+    >>> scores = np.array([0.1, 0.4, 0.35, 0.8])
+    >>> fpr, tpr, thresholds = error_detection_tradeoff(y, scores, pos_label=2)
+    >>> fpr
+    array([ 0.5,  0.5,  0. ])
+    >>> fnr
+    array([ 0. ,  0.5,  0.5])
+    >>> thresholds
+    array([ 0.35,  0.4 ,  0.8 ])
+
 .. _zero_one_loss:
 
 Zero one loss
diff --git a/sklearn/metrics/__init__.py b/sklearn/metrics/__init__.py
index cae8f9b6c7d03..e49cc112a6f7d 100644
--- a/sklearn/metrics/__init__.py
+++ b/sklearn/metrics/__init__.py
@@ -7,6 +7,7 @@
 from .ranking import auc
 from .ranking import average_precision_score
 from .ranking import coverage_error
+from .ranking import detection_error_tradeoff
 from .ranking import label_ranking_average_precision_score
 from .ranking import label_ranking_loss
 from .ranking import precision_recall_curve
@@ -74,6 +75,7 @@
     'confusion_matrix',
     'consensus_score',
     'coverage_error',
+    'detection_error_tradeoff',
     'euclidean_distances',
     'explained_variance_score',
     'f1_score',
diff --git a/sklearn/metrics/ranking.py b/sklearn/metrics/ranking.py
index 793c5a5b3d0ca..abec26ea90755 100644
--- a/sklearn/metrics/ranking.py
+++ b/sklearn/metrics/ranking.py
@@ -15,6 +15,7 @@
 #          Lars Buitinck
 #          Joel Nothman <joel.nothman@gmail.com>
 #          Noel Dawe <noel@dawe.me>
+#          Jeremy Karnowski <jeremy.karnowski@gmail.com>
 # License: BSD 3 clause
 
 from __future__ import division
@@ -184,6 +185,84 @@ def _binary_average_precision(y_true, y_score, sample_weight=None):
                                  average, sample_weight=sample_weight)
 
 
+def detection_error_tradeoff(y_true, y_score, pos_label=None,
+                             sample_weight=None):
+    """Compute error rates for different probability thresholds
+
+    Note: this implementation is restricted to the binary classification task.
+
+    Parameters
+    ----------
+    y_true : array, shape = [n_samples]
+        True targets of binary classification in range {-1, 1} or {0, 1}.
+
+    y_score : array, shape = [n_samples]
+        Estimated probabilities or decision function.
+
+    pos_label : int, optional (default=None)
+        The label of the positive class
+
+    sample_weight : array-like of shape = [n_samples], optional
+        Sample weights.
+
+    Returns
+    -------
+    fps : array, shape = [n_thresholds]
+        A count of false positives, at index i being the number of negative
+        samples assigned a score >= thresholds[i]. The total number of
+        negative samples is equal to fps[-1] (thus true negatives are given by
+        fps[-1] - fps).
+
+    fns : array, shape = [n_thresholds]
+        A count of false negatives, at index i being the number of positive
+        samples assigned a score < thresholds[i]. The total number of
+        positive samples is equal to tps[-1] (thus false negatives are given by
+        tps[-1] - tps).
+
+    thresholds : array, shape = [n_thresholds]
+        Decreasing score values.
+
+    References
+    ----------
+    .. [1] `Wikipedia entry for Detection error tradeoff
+            <https://en.wikipedia.org/wiki/Detection_error_tradeoff>`_
+    .. [2] `The DET Curve in Assessment of Detection Task Performance
+            <http://www.itl.nist.gov/iad/mig/publications/storage_paper/det.pdf>`_
+    .. [3] `2008 NIST Speaker Recognition Evaluation Results
+            <http://www.itl.nist.gov/iad/mig/tests/sre/2008/official_results/>`_
+    .. [4] `DET-Curve Plotting software for use with MATLAB
+            <http://www.itl.nist.gov/iad/mig/tools/DETware_v2.1.targz.htm>`_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.metrics import detection_error_tradeoff
+    >>> y_true = np.array([0, 0, 1, 1])
+    >>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
+    >>> fps, fns, thresholds = detection_error_tradeoff(y_true, y_scores)
+    >>> fps
+    array([ 0.5,  0.5,  0. ])
+    >>> fns
+    array([ 0. ,  0.5,  0.5])
+    >>> thresholds
+    array([ 0.35,  0.4 ,  0.8 ])
+
+    """
+    fps, tps, thresholds = _binary_clf_curve(y_true, y_score,
+                                             pos_label=pos_label,
+                                             sample_weight=sample_weight)
+    fns = tps[-1] - tps
+    tp_count = tps[-1]
+    tn_count = (fps[-1] - fps)[0]
+
+    # start with false positives is zero and stop with false negatives zero
+    # and reverse the outputs so list of false positives is decreasing
+    last_ind = tps.searchsorted(tps[-1]) + 1
+    first_ind = fps[::-1].searchsorted(fps[0])
+    sl = range(first_ind, last_ind)[::-1]
+    return fps[sl] / tp_count, fns[sl] / tn_count, thresholds[sl]
+
+
 def roc_auc_score(y_true, y_score, average="macro", sample_weight=None):
     """Compute Area Under the Curve (AUC) from prediction scores