Rewording for #8

example-data-science-notebook/Example Machine Learning Notebook.ipynb

@@ -1992,7 +1992,7 @@
     "\n",
     "We already know from previous projects that Random Forest classifiers usually work better than individual decision trees. A common problem that decision trees face is that they're prone to overfitting: They complexify to the point that they classify the training set near-perfectly, but fail to generalize to data they have not seen before.\n",
     "\n",
-    "**Random Forests** work around that limitation by creating a whole bunch of decision trees (hence \"forest\") — each trained on a random subset of the features — and have the decision trees work together to make a better classification.\n",
+    "**Random Forest classifiers** work around that limitation by creating a whole bunch of decision trees (hence \"forest\") — each trained on random subsets of training samples (drawn with replacement) and features (drawn without replacement) — and have the decision trees work together to make a more accurate classification.\n",
     "\n",
     "Let that be a lesson for us: **Even in Machine Learning, we get better results when we work together!**\n",
     "\n",