From 940e8de705010bdf234a30f061aeffb4f66e99ba Mon Sep 17 00:00:00 2001
From: Mikolaj Rybinski <mikolaj.rybinski@id.ethz.ch>
Date: Fri, 12 Feb 2021 12:18:05 +0100
Subject: [PATCH] Mention GradientBoostingClassifier and improve
HistGradientBoostingClassifier note
---
06_classifiers_overview-part_2.ipynb | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/06_classifiers_overview-part_2.ipynb b/06_classifiers_overview-part_2.ipynb
index 5d6c813..5181389 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"
]
--
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