diagnosis_time >= DATE_FORMAT(DATE_SUB(NOW(), INTERVAL 1 WEEK), '%Y-%m-%d %H:%i:%s')
时间: 2024-01-23 19:59:57 浏览: 27
As an AI language model, I don't have a personal opinion or feelings. "Null" is a term used in computer programming to represent a value that is absent or undefined.
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target_name = data['diagnosis'] X = data.drop('diagnosis',axis=1) y = target_name解释代码
这段代码是在进行数据预处理,其中:
- `data` 是一个 DataFrame 类型的数据集,其中包含了我们需要使用的特征和目标变量。
- `target_name = data['diagnosis']` 表示将数据集中的名为 `diagnosis` 的列作为目标变量,赋值给 `target_name` 变量。
- `X = data.drop('diagnosis',axis=1)` 表示将除了 `diagnosis` 列之外的所有特征列作为自变量,赋值给 `X` 变量。`axis=1` 表示删除列。
- `y = target_name` 表示将目标变量赋值给 `y` 变量。
换句话说,这段代码的作用是将数据集中的目标变量和自变量分别提取出来,并将自变量存储在 `X` 变量中,将目标变量存储在 `y` 变量中,以便后续进行建模和训练。
改写以下代码,使其计算准确率时分别将误报计算为非有效诊断:accTest1 = sum(testYreal_out==1 & testYreal==1)/sum(testYreal==1); accTest2 = sum(testYreal_out==2 & testYreal==2)/sum(testYreal==2); accTest3 = sum(testYreal_out==3 & testYreal==3)/sum(testYreal==3); accTest4 = sum(testYreal_out==4 & testYreal==4)/sum(testYreal==4);
以下是将误报计算为非有效诊断的改写代码:
total_diagnosis_1 = sum(testYreal==1)
correct_diagnosis_1 = sum(testYreal_out==1 & testYreal==1)
false_positives_1 = sum(testYreal_out==1 & testYreal~=1)
total_diagnosis_2 = sum(testYreal==2)
correct_diagnosis_2 = sum(testYreal_out==2 & testYreal==2)
false_positives_2 = sum(testYreal_out==2 & testYreal~=2)
total_diagnosis_3 = sum(testYreal==3)
correct_diagnosis_3 = sum(testYreal_out==3 & testYreal==3)
false_positives_3 = sum(testYreal_out==3 & testYreal~=3)
total_diagnosis_4 = sum(testYreal==4)
correct_diagnosis_4 = sum(testYreal_out==4 & testYreal==4)
false_positives_4 = sum(testYreal_out==4 & testYreal~=4)
accTest1 = correct_diagnosis_1 / (total_diagnosis_1 - false_positives_1)
accTest2 = correct_diagnosis_2 / (total_diagnosis_2 - false_positives_2)
accTest3 = correct_diagnosis_3 / (total_diagnosis_3 - false_positives_3)
accTest4 = correct_diagnosis_4 / (total_diagnosis_4 - false_positives_4)
accuracy = (correct_diagnosis_1 + correct_diagnosis_2 + correct_diagnosis_3 + correct_diagnosis_4) / (total_diagnosis_1 + total_diagnosis_2 + total_diagnosis_3 + total_diagnosis_4)
false_alarm_rate = (false_positives_1 + false_positives_2 + false_positives_3 + false_positives_4) / (sum(testYreal~=1) + sum(testYreal~=2) + sum(testYreal~=3) + sum(testYreal~=4))