Concrete ML
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  • What is Concrete ML?
  • Getting Started
    • Installation
    • Key Concepts
    • Inference in the Cloud
  • Built-in Models
    • Linear Models
    • Tree-based Models
    • Neural Networks
    • Pandas
    • Built-in Model Examples
  • Deep Learning
    • Using Torch
    • Using ONNX
    • Step-by-Step Guide
    • Deep Learning Examples
    • Debugging Models
  • Advanced topics
    • Quantization
    • Pruning
    • Compilation
    • Production Deployment
    • Advanced Features
  • Developer Guide
    • Workflow
      • Set Up the Project
      • Set Up Docker
      • Documentation
      • Support and Issues
      • Contributing
    • Inner workings
      • Importing ONNX
      • Quantization tools
      • FHE Op-graph design
      • External Libraries
    • API
      • concrete.ml.common
      • concrete.ml.common.check_inputs
      • concrete.ml.common.debugging
      • concrete.ml.common.debugging.custom_assert
      • concrete.ml.common.utils
      • concrete.ml.deployment
      • concrete.ml.deployment.fhe_client_server
      • concrete.ml.onnx
      • concrete.ml.onnx.convert
      • concrete.ml.onnx.onnx_model_manipulations
      • concrete.ml.onnx.onnx_utils
      • concrete.ml.onnx.ops_impl
      • concrete.ml.quantization
      • concrete.ml.quantization.base_quantized_op
      • concrete.ml.quantization.post_training
      • concrete.ml.quantization.quantized_module
      • concrete.ml.quantization.quantized_ops
      • concrete.ml.quantization.quantizers
      • concrete.ml.sklearn
      • concrete.ml.sklearn.base
      • concrete.ml.sklearn.glm
      • concrete.ml.sklearn.linear_model
      • concrete.ml.sklearn.protocols
      • concrete.ml.sklearn.qnn
      • concrete.ml.sklearn.rf
      • concrete.ml.sklearn.svm
      • concrete.ml.sklearn.torch_module
      • concrete.ml.sklearn.tree
      • concrete.ml.sklearn.tree_to_numpy
      • concrete.ml.sklearn.xgb
      • concrete.ml.torch
      • concrete.ml.torch.compile
      • concrete.ml.torch.numpy_module
      • concrete.ml.version
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  • Concrete
  • Concrete ML
  • fhEVM

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  • FHE resources

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  • About
  • Introduction to FHE
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On this page
  • Concrete-ML models
  • Comparison of classifiers

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  1. Built-in Models

Built-in Model Examples

PreviousPandasNextUsing Torch

Last updated 2 years ago

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The following table summarizes the various examples in this section, along with their accuracies.

Model
Data-set
Metric
Floating Point
Simulation
FHE

Linear Regression

Synthetic 1D

R2

0.876

0.863

0.863

Logistic Regression

Synthetic 2D with 2 classes

accuracy

0.90

0.875

0.875

Poisson Regression

mean Poisson deviance

0.61

0.60

0.60

Gamma Regression

mean Gamma deviance

0.45

0.45

0.45

Tweedie Regression

mean Tweedie deviance (power=1.9)

33.42

34.18

34.18

Decision Tree

precision score

0.95

0.97

0.97*

XGBoost Classifier

MCC

0.48

0.52

0.52*

XGBoost Regressor

R2

0.92

0.90

0.90*

A * means that FHE accuracy was calculated on a subset of the validation set.

Concrete-ML models

Comparison of classifiers

LinearRegression.ipynb
LogisticRegression.ipynb
PoissonRegression.ipynb
DecisionTreeClassifier.ipynb
XGBClassifier.ipynb
XGBRegressor.ipynb
GLMComparison.ipynb
ClassifierComparison.ipynb
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