Python模型评估度量

Question

我已经在Python中运行了随机森林模型。我需要将TP、FP、FN和TN数据存储在数据中。

    cf_matrix_train = pd.DataFrame([{'Key':0,'TN':0,'FP':0,'FN':0,'TP':0}])

df = pd.DataFrame()


max_f = list(range(1,3))
max_d = list(range(10,12))



for fea in max_f:
    for dep in max_d:
        clf=RandomForestClassifier(random_state=1,n_estimators=100,max_features=fea, max_depth=dep)
        clf.fit(X_train,y_train)
        train_pred = clf.predict(X_train)
        cm_train = confusion_matrix(train_pred,y_train)
        cf_matrix_train['TN'] = cm_train[0][0]
        cf_matrix_train['FP'] = cm_train[1][0]
        cf_matrix_train['FN'] = cm_train[0][1]
        cf_matrix_train['TP'] = cm_train[1][1]
        cf_matrix_train.append(df)
        print(cm_train)

虽然生成了这四个模型，但只有最后一个模型输出保存到dataframe "cf_matrix_train“中。

当前产出：

预期产出

Answer 1

.append()操作在pandas中不被执行。它返回一个新的数据帧。所以你得把它分配给什么东西。而且，你在做相反的事情。

更换线路

cf_matrix_train.append(df)

使用

df = df.append(cf_matrix_train)

除此之外，你还可以这样做：

df = pd.DataFrame(columns=['Key', 'TN', 'FP', 'FN', 'TP'])

max_f = list(range(1, 3))
max_d = list(range(10, 12))

index = 0
for fea in max_f:
    for dep in max_d:
        clf = RandomForestClassifier(random_state=1, n_estimators=100,
                                     max_features=fea, max_depth=dep)
        clf.fit(X_train,y_train)
        train_pred = clf.predict(X_train)
        cm_train = confusion_matrix(train_pred, y_train)

        df.loc[index, 'Key'] = f"{fea}, {dep}"
        df.loc[index, 'TN'] = cm_train[0][0]
        df.loc[index, 'FP'] = cm_train[1][0]
        df.loc[index, 'FN'] = cm_train[0][1]
        df.loc[index, 'TP'] = cm_train[1][1]

        index += 1