scikit-learn
diff --git a/‎examples/classification/plot_lda.py
Lines changed: 14 additions & 13 deletions b/‎examples/classification/plot_lda.py
Lines changed: 14 additions & 13 deletions
@@ -47,8 +47,8 @@ def generate_data(n_samples, n_features):
     for _ in range(n_averages):
         X, y = generate_data(n_train, n_features)
 
-        clf1 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y)
-        clf2 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None).fit(X, y)
+        clf1 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None).fit(X, y)
+        clf2 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y)
         oa = OAS(store_precision=False, assume_centered=False)
         clf3 = LinearDiscriminantAnalysis(solver="lsqr", covariance_estimator=oa).fit(
             X, y
@@ -69,36 +69,37 @@ def generate_data(n_samples, n_features):
     features_samples_ratio,
     acc_clf1,
     linewidth=2,
-    label="Linear Discriminant Analysis with Ledoit Wolf",
-    color="navy",
-    linestyle="dashed",
+    label="LDA",
+    color="gold",
+    linestyle="solid",
 )
 plt.plot(
     features_samples_ratio,
     acc_clf2,
     linewidth=2,
-    label="Linear Discriminant Analysis",
-    color="gold",
-    linestyle="solid",
+    label="LDA with Ledoit Wolf",
+    color="navy",
+    linestyle="dashed",
 )
 plt.plot(
     features_samples_ratio,
     acc_clf3,
     linewidth=2,
-    label="Linear Discriminant Analysis with OAS",
+    label="LDA with OAS",
     color="red",
     linestyle="dotted",
 )
 
 plt.xlabel("n_features / n_samples")
 plt.ylabel("Classification accuracy")
 
-plt.legend(loc=3, prop={"size": 12})
+plt.legend(loc="lower left")
+plt.ylim((0.65, 1.0))
 plt.suptitle(
-    "Linear Discriminant Analysis vs. "
+    "LDA (Linear Discriminant Analysis) vs. "
     + "\n"
-    + "Shrinkage Linear Discriminant Analysis vs. "
+    + "LDA with Ledoit Wolf vs. "
     + "\n"
-    + "OAS Linear Discriminant Analysis (1 discriminative feature)"
+    + "LDA with OAS (1 discriminative feature)"
 )
 plt.show()