Hi,

My model is a simple decision tree used as a classifier with no parameter sets except the n_bits. My data are generated by the ‘make_classification’ function from scikit-learn with various numbers of features.

My model has a max precision of 7 bits for n_bits=7, 8 bits for n_bits = 8 , etc…

Here is the graph of my model for n_bits = 7 :

```
%0 = _inputs # EncryptedTensor<uint7, shape=(1, 2)> ∈ [0, 127]
%1 = transpose(%0) # EncryptedTensor<uint7, shape=(2, 1)> ∈ [0, 127]
%2 = [[1 0] [1 ... 1] [0 1]] # ClearTensor<uint1, shape=(20, 2)> ∈ [0, 1]
%3 = matmul(%2, %1) # EncryptedTensor<uint7, shape=(20, 1)> ∈ [0, 127]
%4 = [[59] [35] ... [91] [93]] # ClearTensor<uint7, shape=(20, 1)> ∈ [21, 93]
%5 = less_equal(%3, %4) # EncryptedTensor<uint1, shape=(20, 1)> ∈ [0, 1]
%6 = reshape(%5, newshape=[ 1 20 -1]) # EncryptedTensor<uint1, shape=(1, 20, 1)> ∈ [0, 1]
%7 = [[[ 1 1 ... 0 -1 -1]]] # ClearTensor<int2, shape=(1, 21, 20)> ∈ [-1, 1]
%8 = matmul(%7, %6) # EncryptedTensor<int4, shape=(1, 21, 1)> ∈ [-3, 6]
%9 = reshape(%8, newshape=[21 -1]) # EncryptedTensor<int4, shape=(21, 1)> ∈ [-3, 6]
%10 = [[4] [4] [ ... ] [1] [0]] # ClearTensor<uint3, shape=(21, 1)> ∈ [0, 6]
%11 = equal(%10, %9) # EncryptedTensor<uint1, shape=(21, 1)> ∈ [0, 1]
%12 = reshape(%11, newshape=[ 1 21 -1]) # EncryptedTensor<uint1, shape=(1, 21, 1)> ∈ [0, 1]
%13 = [[[127 0 ... 0 127]]] # ClearTensor<uint7, shape=(1, 2, 21)> ∈ [0, 127]
%14 = matmul(%13, %12) # EncryptedTensor<uint7, shape=(1, 2, 1)> ∈ [0, 127]
%15 = reshape(%14, newshape=[ 1 2 -1]) # EncryptedTensor<uint7, shape=(1, 2, 1)> ∈ [0, 127]
%16 = transpose(%15, axes=(2, 1, 0)) # EncryptedTensor<uint7, shape=(1, 2, 1)> ∈ [0, 127]
return %16
```

Where can I find more informations about these FHE execution types ?

Thanks

NB : Just switch the verbose option on true during compilation and got these information about crypto-parameters, is this related to the execution types you mentioned above ?

For n_bits=7 :

```
-- Dag Solution
1x glwe_dimension
2**14 polynomial (16384)
942 lwe dimension
keyswitch l,b=6,3
blindrota l,b=3,11
wopPbs : false
```

For n_bits=8 :

```
-- Dag Solution
2x glwe_dimension
2**10 polynomial (1024)
713 lwe dimension
keyswitch l,b=7,2
blindrota l,b=5,8
wopPbs : true
|cb_decomp l,b=2,10
|pp_decomp l,b=3,13
```