H2O4GPU和Scikit-Learn的分类分数不同

Question

我已经开始用精确性和回忆性来评估一个随机森林分类器。然而，尽管训练和测试集在分类器的CPU和GPU实现上是相同的，但我看到了返回的评估分数的差异。这是一个已知的错误在图书馆偶然？

这两个代码示例都在下面供参考。

Scikit-学习(CPU)

from sklearn.metrics import recall_score, precision_score
from sklearn.ensemble import RandomForestClassifier

rf_cpu = RandomForestClassifier(n_estimators=5000, n_jobs=-1)
rf_cpu.fit(X_train, y_train)
rf_cpu_pred = clf.predict(X_test)

recall_score(rf_cpu_pred, y_test)
precision_score(rf_cpu_pred, y_test)

CPU Recall: 0.807186
CPU Precision: 0.82095

H2O4GPU (GPU)

from h2o4gpu.metrics import recall_score, precision_score
from h2o4gpu import RandomForestClassifier

rf_gpu = RandomForestClassifier(n_estimators=5000, n_gpus=1)
rf_gpu.fit(X_train, y_train)
rf_gpu_pred = clf.predict(X_test)

recall_score(rf_gpu_pred, y_test)
precision_score(rf_gpu_pred, y_test)

GPU Recall: 0.714286
GPU Precision: 0.809988

Answer 1

修正:实现了精确性和召回性输入的顺序错误。按照Scikit-Learn 文档的说法，订单总是(y_true, y_pred)。

修正的评估代码

recall_score(y_test, rf_gpu_pred)
precision_score(y_test, rf_gpu_pred)