adding Intel(R)
@@ -197,7 +197,7 @@ class RandomForestClassifier(skl_RandomForestClassifier):
min_impurity_split=self.min_impurity_split,
random_state=None)
- # we need to set est.tree_ field with Trees constructed from DAAL solution
+ # we need to set est.tree_ field with Trees constructed from Intel(R) DAAL solution
estimators_ = []
for i in range(self.n_estimators):
# print("Tree #{}".format(i))
@@ -320,7 +320,7 @@ class RandomForestRegressor(skl_RandomForestRegressor):
"setting for min_samples_leaf.")
- # daal only supports "mse" criterion
+ # Intel(R) daal only supports "mse" criterion
def daal_fit(self, X, y):
self._check_daal_supported_parameters()
_supported_dtypes_ = [np.double, np.single]
@@ -391,7 +391,7 @@ class RandomForestRegressor(skl_RandomForestRegressor):
min_impurity_split=self.min_impurity_split,
random_state=None)
- # we need to set est.tree_ field with Trees constructed from DAAL solution
+ # we need to set est.tree_ field with Trees constructed from Intel(R) DAAL solution
estimators_ = []
for i in range(self.n_estimators):
est_i = clone(est)
