Java研究API方法(Java look into API method)

 除了迭戈的回答： 
 
 RandomForestClassifier是一个分类器，用于预测离散数量的类的类分配，而无需在类标签之间进行排序。 
 
 如果你想输出连续的浮点数，你应该尝试使用RandomForestRegressor等回归模型。 
 
 您可能必须将输出限制在范围[0，6]，因为没有保证，模型不会输出预测值，例如6.2 。 
 
 编辑以回答第二点， predict方法需要样本清单。 因此，您应该为您的案例提供一份样品清单。 尝试： 
 
print rf.predict([testdataset[-1]])
 
 或者可选地： 
 
print rf.predict(testdataset[-1:])
 
 我想知道你为什么在这种情况下不会出错。 
 
 编辑 ：输出没有意义：数据集的形状是什么？ 
 
>>> print np.asarray(train).shape

>>> print np.asarray(target).shape

>>> print np.asarray(testdataset).shape

In addition to Diego's answer:
 
RandomForestClassifier is a classifier to predict class assignment for a discrete number of classes without ordering between the class labels.
 
If you want to output continuous, floating point rating, you should try to use a regression model such as RandomForestRegressor instead.
 
You might have to clamp the output to the range [0, 6] as there is no guaranty the model will not output predictions such as 6.2 for instance.
 
Edit to answer you second point, the predict method expects a list of samples. Hence you should provide it with a list of one sample in your case. Try:
 
print rf.predict([testdataset[-1]])
 
or alternatively:
 
print rf.predict(testdataset[-1:])
 
I wonder why you don't get an error in that case.
 
Edit: the ouput does not really make sense: what is the shape of your datasets?
 
>>> print np.asarray(train).shape

>>> print np.asarray(target).shape

>>> print np.asarray(testdataset).shape