The following table summarizes the various examples in this section, along with their accuracies.
| Model | Dataset | Metric | Clear | Quantized | FHE |
|---|---|---|---|---|---|
A * means that FHE accuracy was calculated on a subset of the validation set.
In this table, ** means that the accuracy is actually random-like, because the quantization we need to set to fullfill bitsize constraints is too strong.
| Model | Dataset | Metric | Clear | Quantized | 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
1.38
1.68
1.68
Decision Tree
precision score
0.95
0.97
0.97*
XGBoost
MCC
0.48
0.52
0.52*
Fully Connected NN
accuracy
0.947
0.895
0.895
Convolutional NN
accuracy
0.90
**
**