diff --git a/06_classifiers_overview-part_2.ipynb b/06_classifiers_overview-part_2.ipynb
index 5d6c8137eda0d56b436c7f335be25b093ec4e300..5181389ddb841d1e6afd4fcb529d5c0ba1dc792a 100644
--- a/06_classifiers_overview-part_2.ipynb
+++ b/06_classifiers_overview-part_2.ipynb
@@ -1224,11 +1224,11 @@
     "\n",
     "In contrast to AdaBoost, GTB relies on using decision trees.\n",
     "\n",
-    "In particular, try out [XGboost](https://xgboost.readthedocs.io/en/latest/); it's a package that won many competitions, cf. [XGboost@Kaggle](https://www.kaggle.com/dansbecker/xgboost). It is not part of scikit-learn, but it offers a scikit-learn API (see https://www.kaggle.com/stuarthallows/using-xgboost-with-scikit-learn ).\n",
+    "In particular, try out [XGboost](https://xgboost.readthedocs.io/en/latest/); it's a package that won many competitions, cf. [XGboost@Kaggle](https://www.kaggle.com/dansbecker/xgboost). It is not part of scikit-learn, but it offers a `scikit-learn` API (see https://www.kaggle.com/stuarthallows/using-xgboost-with-scikit-learn ); a `scikit-learn` equivalent is [`GradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html).\n",
     "\n",
     "#### Histogram-based Gradient Boosting Classification Tree.\n",
     "\n",
-    "Scikit-learn own new fast and performant implementation of boosting based on decision trees; see [`HistGradientBoostingClassifier` documentation](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html).\n",
+    "A new `scikit-learn` implementation of boosting based on decision trees is [`HistGradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html). It is much faster then `GradientBoostingClassifier` for big datasets (`n_samples >= 10 000`).\n",
     "\n",
     "\n"
    ]