使用[`HistGradientBoostingClassifier`](https://scikit-learn.org/1.0/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html) 对mnist数据进行分类,并与GradientBoostingClassifier分类结果进行比较(训练时间与准确率)。
时间: 2024-01-10 18:03:07 浏览: 183
好的,我需要先导入 `HistGradientBoostingClassifier` 并对数据集进行划分。代码如下:
```python
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistGradientBoostingClassifier
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
接下来,我分别使用 `GradientBoostingClassifier` 和 `HistGradientBoostingClassifier` 进行分类,并记录它们的训练时间和准确率。代码如下:
```python
import time
# Gradient Boosting Classifier
start_time = time.time()
gb_clf = GradientBoostingClassifier(n_estimators=100)
gb_clf.fit(X_train, y_train)
gb_y_pred = gb_clf.predict(X_test)
gb_accuracy = accuracy_score(y_test, gb_y_pred)
gb_time = time.time() - start_time
print("Gradient Boosting Classifier accuracy:", gb_accuracy)
print("Time taken:", gb_time)
# HistGradient Boosting Classifier
start_time = time.time()
hgb_clf = HistGradientBoostingClassifier(max_iter=100)
hgb_clf.fit(X_train, y_train)
hgb_y_pred = hgb_clf.predict(X_test)
hgb_accuracy = accuracy_score(y_test, hgb_y_pred)
hgb_time = time.time() - start_time
print("HistGradient Boosting Classifier accuracy:", hgb_accuracy)
print("Time taken:", hgb_time)
```
执行以上代码后,会输出每个分类器的准确率和训练时间。可以看到,`HistGradientBoostingClassifier` 的准确率与 `GradientBoostingClassifier` 相当,但训练时间更短,因为 `HistGradientBoostingClassifier` 使用了一些优化技术。
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