Hi everyone! I´m working on doing differential fuzzers to compare concrete-ml and scikit, I have a question about how big the difference can be, for example, running the following code:

```
import numpy as np
import sys
import atheris
from sklearn.linear_model import GammaRegressor as SklearnGammaRegressor
from sklearn.linear_model import PoissonRegressor as SklearnPoissonRegressor
from sklearn.linear_model import TweedieRegressor as SklearnTweedieRegressor
from sklearn.model_selection import train_test_split
from concrete.ml.sklearn import GammaRegressor as ConcreteGammaRegressor
from concrete.ml.sklearn import PoissonRegressor as ConcretePoissonRegressor
from concrete.ml.sklearn import TweedieRegressor as ConcreteTweedieRegressor
# Split the data-set into a train and test set,
# each set is split into input and result.
train_data, test_data, x_train_data, x_test_data = train_test_split(**dataset, x, test_size=0.2, random_state=42)
def compare_models(input_bytes):
fdp = atheris.FuzzedDataProvider(input_bytes)
data = [fdp.ConsumeFloatListInRange(74, 0.0, 1.0) for _ in range(15)]
# Instantiate the models
sklearn_glm = SklearnGammaRegressor(**init_parameters)
concrete_glm = ConcreteGammaRegressor(**init_parameters)
# Fit the models
sklearn_glm.fit(**fit_parameters)
concrete_glm.fit(**fit_parameters)
# Compute the predictions using sklearn (floating points, in the clear)
sklearn_predictions = sklearn_glm.predict(data)
# Compute the predictions using COncrete-ML (in FHE)
concrete_predictions = concrete_glm.predict(
data,
execute_in_fhe=True,
).flatten()
print(sklearn_predictions)
print(concrete_predictions)
assert np.allclose(sklearn_predictions, concrete_predictions, atol=1), f"Error: The predictions are different, scikit prediction {sklearn_predictions}; concrete prediction {concrete_predictions}"
atheris.Setup(sys.argv, compare_models)
atheris.Fuzz()
```

When comparing the results, I set a difference of 1 (provisory), but the results difference is a lot smaller, as an example this two:

```
sklearn_predictions= [0.01807652 0.01807652 0.01807652 0.01807652 0.01807652 0.01807652
0.01807652 0.01807652 0.01807652 0.01807652 0.01807652 0.01807652
0.01807652 0.01807652 0.01807652]
concrete_predictions= [0.01808741 0.01808741 0.01808741 0.01808741 0.01808741 0.01808741
0.01808741 0.01808741 0.01808741 0.01808741 0.01808741 0.01808741
0.01808741 0.01808741 0.01808741]
```

can you guide me a little on how big the difference has to be considered valid?