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scikit-learn随机森林:严重过度拟合?

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我试图应用knn,逻辑回归,决策树和随机森林来预测二元响应变量 .

前三个产生看似合理的准确率,但运行随机森林算法产生的准确率超过99%(1127/1128正确) .

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,我在我的数据上拟合模型:

# 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度拟合:任何想法?

1 回答

  • 0

    我担心这里发生了一些严重的过度拟合:任何想法?

    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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