Built-in Model Examples

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	OpenML insurance (freq)	mean Poisson deviance	0.61	0.60	0.60
Gamma Regression	OpenML insurance (sev)	mean Gamma deviance	0.45	0.45	0.45
Tweedie Regression	OpenML insurance (sev)	mean Tweedie deviance (power=1.9)	33.42	34.18	34.18
Decision Tree	OpenML spams	precision score	0.95	0.97	0.97*
XGBoost Classifier	Diabetes	MCC	0.48	0.52	0.52*
XGBoost Regressor	House Prices	R2	0.92	0.90	0.90*

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

Concrete-ML models

• LinearRegression.ipynb

• LogisticRegression.ipynb

• PoissonRegression.ipynb

• DecisionTreeClassifier.ipynb

• XGBClassifier.ipynb

• XGBRegressor.ipynb

• GLMComparison.ipynb

Comparison of classifiers

• ClassifierComparison.ipynb