application_vnd.jupyter.stderr_output_1.png
from sklearn.metrics import accuracy_score

from sklearn.metrics import accuracy_score

import cuml as np

import numpy as np # linear algebra

n_samples = 1000
n_features = 10
n_classes = 2
n_estimators = 25
max_depth = 10
model = cuRF(max_depth=max_depth, n_estimators=n_estimators, random_state=0)
trained_RF = model.fit(X_train, y_train)

predictions = model.predict(X_test)

from cuml.datasets.classification import make_classification
from sklearn.metrics import accuracy_score
cu_score = np.metrics.accuracy_score(y_test, predictions)
sk_score = accuracy_score(y_test.get(), predictions.get())

print(predictions)