From 3cfba99c7cd15933eff4f0a9e357b2b1abcf973d Mon Sep 17 00:00:00 2001 From: Jeremy Bradley-Silverio Donato Date: Thu, 6 Jan 2022 17:44:42 +0100 Subject: [PATCH] docs: Update DecisionTreeClassifier.ipynb --- docs/user/advanced_examples/DecisionTreeClassifier.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/user/advanced_examples/DecisionTreeClassifier.ipynb b/docs/user/advanced_examples/DecisionTreeClassifier.ipynb index cfb649411..6300704c7 100644 --- a/docs/user/advanced_examples/DecisionTreeClassifier.ipynb +++ b/docs/user/advanced_examples/DecisionTreeClassifier.ipynb @@ -13,7 +13,7 @@ "source": [ "Trees are a popular class of algorithm in Machine Learning. In this notebook we build a simple Decision Tree Classifier using `scikit-learn` to show that they can be executed homomorphically using Concrete Numpy.\n", "\n", - "State of the art classifiers are generally a bit more complex than a single decision tree, here we wanted to demonstrate FHE decision trees so results may not compete with the best models out there!\n", + "State of the art classifiers are generally a bit more complex than a single decision tree, but here we wanted to demonstrate FHE decision trees so results may not compete with the best models out there.\n", "\n", "Converting a tree working over quantized data to its FHE equivalent takes only a few lines of code thanks to Concrete Numpy.\n", "\n", @@ -614,7 +614,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook we showed how to quantize a dataset to train a tree directly on integer data so that it's FHE friendly. We saw that despite quantization and its smaller depth the quantized tree classification capabilities were close to a tree trained on the original real-valued dataset.\n", + "In this notebook we showed how to quantize a dataset to train a tree directly on integer data so that it is FHE friendly. We saw that despite quantization and its smaller depth, the quantized tree classification capabilities were close to a tree trained on the original real-valued dataset.\n", "\n", "We then used the Hummingbird paper's algorithm to transform a tree evaluation to a few tensor operations which can be compiled by the Concrete Numpy to an FHE circuit.\n", "\n",