Question

我正在尝试使用glm在我的逻辑回归模型的训练数据上找到AUC

我将数据拆分为训练和测试集，使用glm拟合了Logistic回归模型回归模型，计算了预测值并尝试找到AUC

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    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_insert__orari);

        lunedi_inizio_uno = (EditText)findViewById(R.id.editText_uno_lunedi_inizio);

        lunediUno = (CheckBox)findViewById(R.id.checkBox_uno_lunedi);

        lunedi_inizio_uno.addTextChangedListener(new TextWatcher() {

                    int keyDell;

                    @Override
                    public void onTextChanged(CharSequence s, int start, int before, int count) {

                        lunedi_inizio_uno.setOnKeyListener(new View.OnKeyListener() {
                            @Override
                            public boolean onKey(View v, int keyCode, KeyEvent event) {

                                if (keyCode == KeyEvent.KEYCODE_DEL)
                                    keyDell = 1;
                                int prevL = 0;
                                return false;
                            }
                        });

                        if (keyDell == 0) {
                            int len = lunedi_inizio_uno.getText().length();

                            if(len == 5) {
                                lunedi_inizio_uno.setText(lunedi_inizio_uno.getText() + " ");
                                lunedi_inizio_uno.setSelection(lunedi_inizio_uno.getText().length());
                            }if(len == 6) {
                                lunedi_inizio_uno.setText(lunedi_inizio_uno.getText() + "-");
                                lunedi_inizio_uno.setSelection(lunedi_inizio_uno.getText().length());
                            }if(len == 7) {
                                lunedi_inizio_uno.setText(lunedi_inizio_uno.getText() + " ");
                                lunedi_inizio_uno.setSelection(lunedi_inizio_uno.getText().length());
                            }
                        } else {
                            keyDell = 0;
                        }

                    }

                    @Override
                    public void afterTextChanged(Editable arg0) {
                                    }

                    @Override
                    public void beforeTextChanged(CharSequence arg0, int arg1, int arg2, int arg3) {
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                });

                lunediUno.setOnClickListener(new View.OnClickListener() {
                            @Override
                            public void onClick(View v) {
                                if (((CheckBox) v).isChecked()) {
                                    lunedi_inizio_uno.setText("Chiuso");

                                }


                            }
                        });

                    }

Answer 1

我喜欢使用performance库中的ROCR命令。

library(ROCR)
# responsev = response variable

d.prediction<-prediction(predict(fit, type="response"), train$responsev)
d.performance<-performance(d.prediction,measure = "tpr",x.measure="fpr")
d.test.prediction<-prediction(predict(fit,newdata=d.test, type="response"), d.test$DNF)
d.test.prefermance<-performance(d.test.prediction, measure="tpr", x.measure="fpr")

# What is the actual numeric performance of our model?
performance(d.prediction,measure="auc")
performance(d.test.prediction,measure="auc")

Answer 2

另一个用户友好的选择是使用caret库，这使得在R中拟合和比较回归/分类模型非常简单。以下示例代码使用GermanCredit数据集来预测信用使用逻辑回归模型的价值。该代码改编自以下博客：https://www.r-bloggers.com/evaluating-logistic-regression-models/。

library(caret)

## example from https://www.r-bloggers.com/evaluating-logistic-regression-models/
data(GermanCredit)

## 60% training / 40% test data
trainIndex <- createDataPartition(GermanCredit$Class, p = 0.6, list = FALSE)

GermanCreditTrain <- GermanCredit[trainIndex, ]
GermanCreditTest <- GermanCredit[-trainIndex, ]

## logistic regression based on 10-fold cross-validation 
trainControl <- trainControl(
     method = "cv",
     number = 10,
     classProbs = TRUE,
     summaryFunction = twoClassSummary
)

fit <- train(
    form = Class ~ Age + ForeignWorker + Property.RealEstate + Housing.Own + 
         CreditHistory.Critical,  
    data = GermanCreditTrain,
    trControl = trainControl,
    method = "glm", 
    family = "binomial", 
    metric = "ROC"
)

## AUC ROC for training data
print(fit)

## AUC ROC for test data
## See https://topepo.github.io/caret/measuring-performance.html#measures-for-class-probabilities
 predictTest <- data.frame(
         obs = GermanCreditTest$Class,                                    ## observed class labels
         predict(fit, newdata = GermanCreditTest, type = "prob"),         ## predicted class probabilities
         pred = predict(fit, newdata = GermanCreditTest, type = "raw")    ## predicted class labels
     ) 

twoClassSummary(data = predictTest, lev = levels(predictTest$obs))

在R中为glm函数计算训练数据集的AUC

2 个答案: