[英]scikit-learn random forest: severe overfitting?
我正在尝试应用knn,逻辑回归,决策树和随机森林来预测二进制响应变量。
前三个产生看似合理的准确率,但是运行随机森林算法产生的准确率超过99%(正确的1127/1128)。
vote_lst = list(range(1, 101))
rf_cv_scores = []
for tree_count in vote_lst:
maple = RandomForestClassifier(n_estimators = tree_count, random_state = 1618)
scores = cross_val_score(maple, x, y, cv = 10, scoring = 'accuracy') # 10-fold CV
rf_cv_scores.append(scores.mean())
# find minimum error's index (i.e. optimal num. of estimators)
rf_MSE = [1 - x for x in rf_cv_scores]
min_error = rf_MSE[0]
for i in range(len(rf_MSE)):
min_error = min_error
if rf_MSE[i] < min_error:
rf_min_index = i
min_error = rf_MSE[i]
print(rf_min_index + 1) # error minimized w/ 66 estimators
我使用上面的代码调整了RF算法超参数n_estimators
。 然后,将模型拟合到我的数据中:
# fit random forest classifier
forest_classifier = RandomForestClassifier(n_estimators = rf_min_index + 1, random_state = 1618)
forest_classifier.fit(x, y)
# predict test set
y_pred_forest = forest_classifier.predict(x)
我担心这里发生了一些严重的过拟合:有什么想法吗?
我担心这里发生了一些严重的过拟合:有什么想法吗?
您正在对上面训练过的同一数据集进行预测:
y_pred_forest = forest_classifier.predict(x)
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