FIX Add missing "be" in cross-validation-curve notebook

notebooks/cross_validation_validation_curve.ipynb

@@ -255,18 +255,18 @@
     "errors made during the data collection process (besides not measuring the\n",
     "unobserved input feature).\n",
     "\n",
-    "One extreme case could happen if there where samples in the dataset with\n",
-    "exactly the same input feature values but different values for the target\n",
-    "variable. That is very unlikely in real life settings, but could the case if\n",
-    "all features are categorical or if the numerical features were discretized\n",
-    "or rounded up naively. In our example, we can imagine two houses having\n",
-    "the exact same features in our dataset, but having different prices because\n",
-    "of the (unmeasured) seller's rush.\n",
-    "\n",
-    "Apart from these extreme case, it's hard to know for sure what should qualify\n",
-    "or not as noise and which kind of \"noise\" as introduced above is dominating.\n",
-    "But in practice, the best ways to make our predictive models robust to noise\n",
-    "are to avoid overfitting models by:\n",
+    "One extreme case could happen if there where samples in the dataset with exactly\n",
+    "the same input feature values but different values for the target variable. That\n",
+    "is very unlikely in real life settings, but could be the case if all features\n",
+    "are categorical or if the numerical features were discretized or rounded up\n",
+    "naively. In our example, we can imagine two houses having the exact same\n",
+    "features in our dataset, but having different prices because of the (unmeasured)\n",
+    "seller's rush.\n",
+    "\n",
+    "Apart from this extreme case, it's hard to know for sure what should qualify or\n",
+    "not as noise and which kind of \"noise\" as introduced above is dominating. But in\n",
+    "practice, the best way to make our predictive models robust to noise is to\n",
+    "avoid overfitting models by:\n",
     "\n",
     "- selecting models that are simple enough or with tuned hyper-parameters as\n",
     "  explained in this module;\n",

python_scripts/cross_validation_validation_curve.py

@@ -198,16 +198,16 @@
 #
 # One extreme case could happen if there where samples in the dataset with
 # exactly the same input feature values but different values for the target
-# variable. That is very unlikely in real life settings, but could the case if
-# all features are categorical or if the numerical features were discretized
-# or rounded up naively. In our example, we can imagine two houses having
-# the exact same features in our dataset, but having different prices because
-# of the (unmeasured) seller's rush.
+# variable. That is very unlikely in real life settings, but could be the case
+# if all features are categorical or if the numerical features were discretized
+# or rounded up naively. In our example, we can imagine two houses having the
+# exact same features in our dataset, but having different prices because of the
+# (unmeasured) seller's rush.
 #
-# Apart from these extreme case, it's hard to know for sure what should qualify
+# Apart from this extreme case, it's hard to know for sure what should qualify
 # or not as noise and which kind of "noise" as introduced above is dominating.
-# But in practice, the best ways to make our predictive models robust to noise
-# are to avoid overfitting models by:
+# But in practice, the best way to make our predictive models robust to noise
+# is to avoid overfitting models by:
 #
 # - selecting models that are simple enough or with tuned hyper-parameters as
 #   explained in this module;