我如何解释 neurolab 模拟的输出？

Question

我正在使用 neurolab 模拟神经网络将数据集分类为二元分类。

我有 dataframe.I 中的数据，我正在创建一个具有一个输入值和一个输出值以及 10 个隐藏节点的神经网络。

df_train = pd.read_csv("training.csv")
target = df_train['outputcol'] # already encoded into 0's and 1's
inp = df_train.INPUT_AMT.values.reshape(df_train.INPUT_AMT.count(),1)
output = target.reshape(len(target),1) #reshaping into a matrix

然后我创建一个模型，计算min_input和max_input：

net = nl.net.newff([[min_input,max_imput]], [10,1]) 
error = net.train(inp,output)
out = net.sim(inp)

这是变量的内容out:

array([[ 0.46434608],
   [ 0.47084458],
   [ 0.46583954],
   ..., 
   [ 0.46898838],
   [ 0.22519667],
   [ 0.46541441]])

这应该如何解释？

Answer 1

我们 Piazza 页面（加州大学伯克利分校，INFO290t）上的答案似乎是正确的：

I believe you're supposed to round the output to zero or one and then compare the rounded results with your target to determine the accuracy.

"If the output of your classifier is a continuous value between 0 and 1, such as with a neural network with sigmoid output node, then you can round the output. For example, if a prediction of 0.80 was made, this would round to 1 (Romney)... For Neural Networks, you should map Obama to 0 and Romney to 1."

我如何解释 neurolab 模拟的输出？

How do I interpret the output of a neurolab simulation?

python

data-mining

neural-network