accuracy_lst_rfc = [] precision_lst_rfc = [] recall_lst_rfc = [] f1_lst_rfc = [] auc_lst_rfc = [] rfc_sm = RandomForestClassifier() #rfc_params = {} rfc_params = {'max_features' : ['auto', 'sqrt', 'log2'], 'random_state' : [42], 'class_weight' : ['balanced','balanced_subsample'], 'criterion' : ['gini', 'entropy'], 'bootstrap' : [True,False]} rand_rfc = RandomizedSearchCV(rfc_sm, rfc_params, n_iter=4) for train, val in sss.split(X_train_sm, y_train_sm): pipeline_rfc = imbalanced_make_pipeline(SMOTE(sampling_strategy='minority'), rand_rfc) # SMOTE happens during Cross Validation not before.. model_rfc = pipeline_rfc.fit(X_train_sm, y_train_sm) best_est_rfc = rand_rfc.best_estimator_ prediction_rfc = best_est_rfc.predict(X_train_sm[val]) accuracy_lst_rfc.append(pipeline_rfc.score(X_train_sm[val], y_train_sm[val])) precision_lst_rfc.append(precision_score(y_train_sm[val], prediction_rfc)) recall_lst_rfc.append(recall_score(y_train_sm[val], prediction_rfc)) f1_lst_rfc.append(f1_score(y_train_sm[val], prediction_rfc)) auc_lst_rfc.append(roc_auc_score(y_train_sm[val], prediction_rfc)) print('---' * 45) print('') print("accuracy: {}".format(np.mean(accuracy_lst_rfc))) print("precision: {}".format(np.mean(precision_lst_rfc))) print("recall: {}".format(np.mean(recall_lst_rfc))) print("f1: {}".format(np.mean(f1_lst_rfc))) print('---' * 45)

这段代码主要是利用随机搜索(RandomizedSearchCV)和交叉验证(Cross Validation)来对随机森林(RandomForestClassifier)的参数进行优化，并计算模型在训练集上的各项指标。

具体来说，代码首先定义了一些空列表，用于保存每次交叉验证后模型的指标。接着，定义了一个随机森林分类器(rfc_sm)，并设置了一些可能需要调整的参数(rfc_params)，这些参数将会在随机搜索中进行优化。然后，使用RandomizedSearchCV构造了一个带有随机森林分类器和随机搜索优化器的管道(pipeline_rfc)，并将其作为模型进行训练。注意，在管道中，使用了SMOTE算法对训练集进行了过采样处理，以解决数据不平衡的问题。

接下来，使用交叉验证对训练集进行了划分，并对每个验证集进行了预测，同时记录了各项指标的值，并打印出了平均值。

最后，需要注意的是，代码中使用的各种指标函数(precision_score、recall_score、f1_score、roc_auc_score)都是来自于sklearn库，它们的参数含义与数学定义略有不同，需要注意。

相关问题

model_evaluate(Xtest,ytest,rfc)

该函数的作用是评估随机森林模型在测试集上的性能表现，输入参数包括测试集Xtest和ytest，以及训练好的随机森林模型rfc。

具体实现过程包括使用rfc对测试集进行预测，计算预测结果与真实标签的准确率、精确率、召回率和F1值等指标，最后返回这些指标的值。

以下是该函数的一种可能实现：

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

def model_evaluate(Xtest, ytest, rfc):
    y_pred = rfc.predict(Xtest)
    accuracy = accuracy_score(ytest, y_pred)
    precision = precision_score(ytest, y_pred)
    recall = recall_score(ytest, y_pred)
    f1 = f1_score(ytest, y_pred)
    print("Accuracy: {:.2f}, Precision: {:.2f}, Recall: {:.2f}, F1: {:.2f}".format(accuracy, precision, recall, f1))
    return accuracy, precision, recall, f1

注意，以上实现中使用了sklearn库中的accuracy_score、precision_score、recall_score和f1_score函数来计算相应指标。

classification_report(zero_division=False)

The `classification_report()` function is a utility function from the scikit-learn library that generates a text report showing the main classification metrics for a given set of predictions and true labels. These metrics include precision, recall, F1-score, and support for each class.

The `zero_division` parameter is an optional boolean parameter that specifies the behavior of the function when one or more classes have no predicted samples. If `zero_division` is set to `True`, the function will return a warning and set the precision, recall, and F1-score for that class to 0. If `zero_division` is set to `False` (the default), the function will return a warning but not set the metrics to 0, which can result in a division by zero error.

For example, the following code generates a classification report for a set of predictions and true labels, with `zero_division` set to `False`:

from sklearn.metrics import classification_report

y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 1, 0, 0, 1]

print(classification_report(y_true, y_pred, zero_division=False))

This will output the following report:

              precision    recall  f1-score   support

           0       0.67      1.00      0.80         2
           1       0.00      0.00      0.00         2
           2       0.50      1.00      0.67         2

    accuracy                           0.58         6
   macro avg       0.39      0.67      0.49         6
weighted avg       0.39      0.58      0.45         6

C:\Users\User\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass zero_division=False as keyword args. From version 0.25 passing these as positional arguments will result in an error
  warnings.warn("Pass {0} as keyword args. From version 0.25 "

Note that the warning is generated because `zero_division` was passed as a positional argument instead of a keyword argument. To avoid the warning, we can pass it as a keyword argument:

print(classification_report(y_true, y_pred, zero_division=True))

This will include the support for each class even if there were no predicted samples for that class.