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# AZBaseClasses.AZClassifier._updateDFVExtremes

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```                probOf1 = score[self.classVar.values[1]]
DFV = -(probOf1-0.5)
self._updateDFVExtremes(DFV)
else:
y_hat = self.classVar(value)
score = Orange.statistics.distribution.Continuous(self.classVar)
score[y_hat] = 1.0
if not value.isSpecial():
DFV = float(value.value)
self._updateDFVExtremes(DFV)
```

```                    #On Regression models assume the DVF as the value predicted
DFV = res
self._updateDFVExtremes(DFV)
res = dataUtilities.CvMat2orangeResponse(res,self.classVar)
else:
```
```                    #On Regression models assume the DVF as the value predicted
DFV = res
self._updateDFVExtremes(DFV)
res = dataUtilities.CvMat2orangeResponse(res,self.classVar)

```

```                # Subtract 0.5 so that the threshold is 0 as all learners DFV
DFV -= 0.5
self._updateDFVExtremes(DFV)

# Retrun the desired quantity
```
```            #On Regression models, assume the DFV as the value predicted
DFV = res.value
self._updateDFVExtremes(DFV)
y_hat = self.classVar(res.value)
dist = Orange.statistics.distribution.Continuous(self.classVar)
```

```                    if not prediction.isSpecial():
DFV = float(prediction.value)
self._updateDFVExtremes(DFV)
y_hat = self.classVar(prediction)
probabilities = Orange.statistics.distribution.Continuous(self.classVar)
```
```    def convert2DFV(self,probOf1):
# Subtract 0.5 so that the threshold is 0 and invert the signal as all learners have standard DFV:
# Positive Values for the first element of the class attributes, and negatove values to the second
DFV = -(probOf1-0.5)
self._updateDFVExtremes(DFV)
```

```                probOf1 = probabilities[self.classVar.values[1]]
DFV = -(probOf1-0.5)
self._updateDFVExtremes(DFV)

else:
#On Regression models assume the DVF as the value predicted
DFV = prediction
self._updateDFVExtremes(DFV)
```

```            #On Regression models assume the DVF as the value predicted
DFV = prediction
self._updateDFVExtremes(DFV)

if resultType == orange.GetBoth:
```

```    def _convert2DFV(self,probOf1):