为什么在CPU（使用SKLearn）和GPU（使用RAPID）上使用RandomForestClassifier会得到不同的分数，却大不相同？

我在使用SKLearn的CPU和使用RAPID的GPU上使用RandomForestClassifier。我正在这两个库之间进行基准测试，以使用Iris数据集进行加速和评分（尝试，将来，我将更改数据集以获得更好的基准测试，我将从这两个库开始）。

问题是，当我在CPU上测量分数时总是得到1.0的值，但是当我在GPU上测量分数时，我得到的变量值在0.2到1.0之间，我不知道为什么会发生这种情况。

首先，我使用的库版本是：

NumPy Version: 1.17.5
Pandas Version: 0.25.3
Scikit-Learn Version: 0.22.1
cuPY Version: 6.7.0
cuDF Version: 0.12.0
cuML Version: 0.12.0
Dask Version: 2.10.1
DaskCuda Version: 0+unknown
DaskCuDF Version: 0.12.0
MatPlotLib Version: 3.1.3
SeaBorn Version: 0.10.0

我用于SKLearn RandomForestClassifier的代码是：

# Read data in host memory
host_s_csv = pd.read_csv('./DataSet/iris.csv', header = 0, delimiter = ',') # Get complete CSV
host_s_data = host_s_csv.iloc[:, [0, 1, 2, 3]].astype('float32') # Get data columns
host_s_labels = host_s_csv.iloc[:, 4].astype('category').cat.codes # Get labels column

# Plot data
#sns.pairplot(host_s_csv, hue = 'variety');

# Split train and test data
host_s_data_train, host_s_data_test, host_s_labels_train, host_s_labels_test = sk_train_test_split(host_s_data, host_s_labels, test_size = 0.2, random_state = 0)

# Create RandomForest model
sk_s_random_forest = skRandomForestClassifier(n_estimators = 40,
                                             max_depth = 16,
                                             max_features = 1.0,
                                             random_state = 10, 
                                             n_jobs = 1)

# Fit data in RandomForest
sk_s_random_forest.fit(host_s_data_train, host_s_labels_train)

# Predict data
sk_s_random_forest_labels_predicted = sk_s_random_forest.predict(host_s_data_test)

# Check score
print('accuracy_score: ', sk_accuracy_score(host_s_labels_test, sk_s_random_forest_labels_predicted))

我用于RAPIDs RandomForestClassifier的代码是：

# Read data in device memory
device_s_csv = cudf.read_csv('./DataSet/iris.csv', header = 0, delimiter = ',') # Get complete CSV
device_s_data = device_s_csv.iloc[:, [0, 1, 2, 3]].astype('float32') # Get data columns
device_s_labels = device_s_csv.iloc[:, 4].astype('category').cat.codes # Get labels column

# Plot data
#sns.pairplot(device_s_csv.to_pandas(), hue = 'variety');

# Split train and test data
device_s_data_train, device_s_data_test, device_s_labels_train, device_s_labels_test = cu_train_test_split(device_s_data, device_s_labels, train_size = 0.8, shuffle = True, random_state = 0)

# Use same data as host
#device_s_data_train = cudf.DataFrame.from_pandas(host_s_data_train)
#device_s_data_test = cudf.DataFrame.from_pandas(host_s_data_test)
#device_s_labels_train = cudf.Series.from_pandas(host_s_labels_train).astype('int32')
#device_s_labels_test = cudf.Series.from_pandas(host_s_labels_test).astype('int32')

# Create RandomForest model
cu_s_random_forest = cusRandomForestClassifier(n_estimators = 40,
                                               max_depth = 16,
                                               max_features = 1.0,
                                               n_streams = 1)

# Fit data in RandomForest
cu_s_random_forest.fit(device_s_data_train, device_s_labels_train)

# Predict data
cu_s_random_forest_labels_predicted = cu_s_random_forest.predict(device_s_data_test)

# Check score
print('accuracy_score: ', cu_accuracy_score(device_s_labels_test, cu_s_random_forest_labels_predicted))

我使用的虹膜数据集的一个示例是：

你知道为什么会这样吗？两种模型设置相同，参数相同，...我不知道为什么分数之间会有如此大的差异。

谢谢。