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docs: Update DecisionTreeClassifier.ipynb

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Jeremy Bradley-Silverio Donato
2022-01-06 17:44:42 +01:00
committed by Benoit Chevallier
parent 814d53e911
commit 3cfba99c7c
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docs/user/advanced_examples/DecisionTreeClassifier.ipynb
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@@ -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",