diff --git a/requirements.txt b/requirements.txt
index 3b6d6e6..951b1fa 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
 matplotlib>=1.4.0
-scikit-learn>=0.18
+scikit-learn>=0.21
 scipy>=0.9
 joblib>=0.10
diff --git a/scikitplot/metrics.py b/scikitplot/metrics.py
index 08ec693..93d2b44 100644
--- a/scikitplot/metrics.py
+++ b/scikitplot/metrics.py
@@ -908,7 +908,7 @@ def plot_silhouette(X, cluster_labels, title='Silhouette Analysis',
     return ax
 
 
-def plot_calibration_curve(y_true, probas_list, clf_names=None, n_bins=10,
+def plot_calibration_curve(y_true, probas_list, clf_names=None, n_bins=10, strategy="uniform",
                            title='Calibration plots (Reliability Curves)',
                            ax=None, figsize=None, cmap='nipy_spectral',
                            title_fontsize="large", text_fontsize="medium"):
@@ -938,6 +938,12 @@ def plot_calibration_curve(y_true, probas_list, clf_names=None, n_bins=10,
         n_bins (int, optional): Number of bins. A bigger number requires more
             data.
 
+        strategy (str, optional): Strategy used to define the widths of the bins.
+            uniform
+                The bins have identical widths.
+            quantile
+                The bins have the same number of samples and depend on `y_prob`.
+
         title (string, optional): Title of the generated plot. Defaults to
             "Calibration plots (Reliabilirt Curves)"
 
@@ -1024,7 +1030,7 @@ def plot_calibration_curve(y_true, probas_list, clf_names=None, n_bins=10,
         probas = (probas - probas.min()) / (probas.max() - probas.min())
 
         fraction_of_positives, mean_predicted_value = \
-            calibration_curve(y_true, probas, n_bins=n_bins)
+            calibration_curve(y_true, probas, n_bins=n_bins, strategy=strategy)
 
         color = plt.cm.get_cmap(cmap)(float(i) / len(probas_list))