Python “ClassificationDataSet”中的“target”有什么好处？

我试图找出

ClassificationDataSet

的参数

target

可以用于什么，但我仍然不清楚

我试过的 由于这不包含有关目标的信息（除了默认值为1），我查看了以下内容：

现在还不清楚，所以我看了一下：

---

这似乎与输出维度有关。但是不应该是

target

然后是

nb\u classes

？

target

参数是训练样本输出维度的维度。为了充分理解it和nb_类之间的区别，让我们看看

\u converttoonefmany

方法：

def _convertToOneOfMany(self, bounds=(0, 1)):
    """Converts the target classes to a 1-of-k representation, retaining the
    old targets as a field `class`.

    To supply specific bounds, set the `bounds` parameter, which consists of
    target values for non-membership and membership."""
    if self.outdim != 1:
        # we already have the correct representation (hopefully...)
        return
    if self.nClasses <= 0:
        self.calculateStatistics()
    oldtarg = self.getField('target')
    newtarg = zeros([len(self), self.nClasses], dtype='Int32') + bounds[0]
    for i in range(len(self)):
        newtarg[i, int(oldtarg[i])] = bounds[1]
    self.setField('target', newtarg)
    self.setField('class', oldtarg)

所以输出的维度等于1，但有两个输出类：0和1。 因此，我们可以将数据更改为：

 IN    OUT
[0,0],(0,1)
[0,1],(1,0)
[1,0],(1,0)
[1,1],(0,1)

现在，输出的第一个参数是

True

的值，第二个参数是

False

的值。 这是一种常见的做法，有更多的课程，例如手写识别

---

希望为您清除此lite位。

为什么要使用更长的输出值表示形式？

class SupervisedDataSet(DataSet):
    """SupervisedDataSets have two fields, one for input and one for the target.
    """

    def __init__(self, inp, target):
        """Initialize an empty supervised dataset.

        Pass `inp` and `target` to specify the dimensions of the input and
        target vectors."""
        DataSet.__init__(self)
        if isscalar(inp):
            # add input and target fields and link them
            self.addField('input', inp)
            self.addField('target', target)
        else:
            self.setField('input', inp)
            self.setField('target', target)

        self.linkFields(['input', 'target'])

        # reset the index marker
        self.index = 0

        # the input and target dimensions
        self.indim = self.getDimension('input')
        self.outdim = self.getDimension('target')

def _convertToOneOfMany(self, bounds=(0, 1)):
    """Converts the target classes to a 1-of-k representation, retaining the
    old targets as a field `class`.

    To supply specific bounds, set the `bounds` parameter, which consists of
    target values for non-membership and membership."""
    if self.outdim != 1:
        # we already have the correct representation (hopefully...)
        return
    if self.nClasses <= 0:
        self.calculateStatistics()
    oldtarg = self.getField('target')
    newtarg = zeros([len(self), self.nClasses], dtype='Int32') + bounds[0]
    for i in range(len(self)):
        newtarg[i, int(oldtarg[i])] = bounds[1]
    self.setField('target', newtarg)
    self.setField('class', oldtarg)

 IN   OUT
[0,0],0
[0,1],1
[1,0],1
[1,1],0

 IN    OUT
[0,0],(0,1)
[0,1],(1,0)
[1,0],(1,0)
[1,1],(0,1)