Question

我有数据文件，其中包含预测MS中录取的数据。它包含9列，第8列包含学生数据，第9列包含选择学生的机会。我是新手，我不理解训练模型中会出现错误。

编译代码后出现错误

import pandas 
import numpy as np 
import sklearn as sl 
from sklearn.neural_network import MLPClassifier
classifier = MLPClassifier()


data = pandas.read_csv('Addmition.csv')
data_array = np.array(data)

X = data_array[:,1:8]
y = data_array[:,8]

classifier.fit(X,y)
print(classifier)

    Traceback (most recent call last):
  File "c.py", line 14, in <module>
    classifier.fit(X,y)
  File "C:\Users\vishal jangid\AppData\Roaming\Python\Python37\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 977, in fit
    hasattr(self, "classes_")))
  File "C:\Users\vishal jangid\AppData\Roaming\Python\Python37\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 324, in _fit
    X, y = self._validate_input(X, y, incremental)
  File "C:\Users\vishal jangid\AppData\Roaming\Python\Python37\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 920, in _validate_input
    self._label_binarizer.fit(y)
  File "C:\Users\vishal jangid\AppData\Roaming\Python\Python37\site-packages\sklearn\preprocessing\label.py", line 413, in fit
    self.classes_ = unique_labels(y)
  File "C:\Users\vishal jangid\AppData\Roaming\Python\Python37\site-packages\sklearn\utils\multiclass.py", line 96, in unique_labels
    raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([0.92, 0.76, 0.72, 0.8 , 0.65, 0.9 , 0.75, 0.68, 0.5 , 0.45, 0.52,
       0.84, 0.78, 0.62, 0.61, 0.54, 0.66, 0.65, 0.63, 0.62, 0.64, 0.7 ,
       0.94, 0.95, 0.97, 0.94, 0.76, 0.44, 0.46, 0.54, 0.65, 0.74, 0.91,
       0.9 , 0.94, 0.88, 0.64, 0.58, 0.52, 0.48, 0.46, 0.49, 0.53, 0.87,
       0.91, 0.88, 0.86, 0.89, 0.82, 0.78, 0.76, 0.56, 0.78, 0.72, 0.7 ,
       0.64, 0.64, 0.46, 0.36, 0.42, 0.48, 0.47, 0.54, 0.56, 0.52, 0.55,
       0.61, 0.57, 0.68, 0.78, 0.94, 0.96, 0.93, 0.84, 0.74, 0.72, 0.74,
       0.64, 0.44, 0.46, 0.5 , 0.96, 0.92, 0.92, 0.94, 0.76, 0.72, 0.66,
       0.64, 0.74, 0.64, 0.38, 0.34, 0.44, 0.36, 0.42, 0.48, 0.86, 0.9 ,
       0.79, 0.71, 0.64, 0.62, 0.57, 0.74, 0.69, 0.87, 0.91, 0.93, 0.68,
       0.61, 0.69, 0.62, 0.72, 0.59, 0.66, 0.56, 0.45, 0.47, 0.71, 0.94,
       0.94, 0.57, 0.61, 0.57, 0.64, 0.85, 0.78, 0.84, 0.92, 0.96, 0.77,
       0.71, 0.79, 0.89, 0.82, 0.76, 0.71, 0.8 , 0.78, 0.84, 0.9 , 0.92,
       0.97, 0.8 , 0.81, 0.75, 0.83, 0.96, 0.79, 0.93, 0.94, 0.86, 0.79,
       0.8 , 0.77, 0.7 , 0.65, 0.61, 0.52, 0.57, 0.53, 0.67, 0.68, 0.81,
       0.78, 0.65, 0.64, 0.64, 0.65, 0.68, 0.89, 0.86, 0.89, 0.87, 0.85,
       0.9 , 0.82, 0.72, 0.73, 0.71, 0.71, 0.68, 0.75, 0.72, 0.89, 0.84,
       0.93, 0.93, 0.88, 0.9 , 0.87, 0.86, 0.94, 0.77, 0.78, 0.73, 0.73,
       0.7 , 0.72, 0.73, 0.72, 0.97, 0.97, 0.69, 0.57, 0.63, 0.66, 0.64,
       0.68, 0.79, 0.82, 0.95, 0.96, 0.94, 0.93, 0.91, 0.85, 0.84, 0.74,
       0.76, 0.75, 0.76, 0.71, 0.67, 0.61, 0.63, 0.64, 0.71, 0.82, 0.73,
       0.74, 0.69, 0.64, 0.91, 0.88, 0.85, 0.86, 0.7 , 0.59, 0.6 , 0.65,
       0.7 , 0.76, 0.63, 0.81, 0.72, 0.71, 0.8 , 0.77, 0.74, 0.7 , 0.71,
       0.93, 0.85, 0.79, 0.76, 0.78, 0.77, 0.9 , 0.87, 0.71, 0.7 , 0.7 ,
       0.75, 0.71, 0.72, 0.73, 0.83, 0.77, 0.72, 0.54, 0.49, 0.52, 0.58,
       0.78, 0.89, 0.7 , 0.66, 0.67, 0.68, 0.8 , 0.81, 0.8 , 0.94, 0.93,
       0.92, 0.89, 0.82, 0.79, 0.58, 0.56, 0.56, 0.64, 0.61, 0.68, 0.76,
       0.86, 0.9 , 0.71, 0.62, 0.66, 0.65, 0.73, 0.62, 0.74, 0.79, 0.8 ,
       0.69, 0.7 , 0.76, 0.84, 0.78, 0.67, 0.66, 0.65, 0.54, 0.58, 0.79,
       0.8 , 0.75, 0.73, 0.72, 0.62, 0.67, 0.81, 0.63, 0.69, 0.8 , 0.43,
       0.8 , 0.73, 0.75, 0.71, 0.73, 0.83, 0.72, 0.94, 0.81, 0.81, 0.75,
       0.79, 0.58, 0.59, 0.47, 0.49, 0.47, 0.42, 0.57, 0.62, 0.74, 0.73,
       0.64, 0.63, 0.59, 0.73, 0.79, 0.68, 0.7 , 0.81, 0.85, 0.93, 0.91,
       0.69, 0.77, 0.86, 0.74, 0.57, 0.51, 0.67, 0.72, 0.89, 0.95, 0.79,
       0.39, 0.38, 0.34, 0.47, 0.56, 0.71, 0.78, 0.73, 0.82, 0.62, 0.96,
       0.96, 0.46, 0.53, 0.49, 0.76, 0.64, 0.71, 0.84, 0.77, 0.89, 0.82,
       0.84, 0.91, 0.67, 0.95]),)

Answer 1

尝试：

import numpy as np 
import sklearn as sl 
from sklearn.neural_network import MLPRegressor
classifier = MLPRegressor()


data = pandas.read_csv('Addmition.csv')
data_array = np.array(data)

X = data_array[:,1:8]
y = data_array[:,8]

classifier.fit(X,y)
print(classifier)

说明：

在机器学习中，我们可能会遇到两种类型的问题：

1）分类：例如：预测一个人是男性还是女性。（离散）

2）回归：例如：预测人的年龄。（连续）

有了这个，我们将看到您的问题，您的标签（选择机会）是连续的，因此我们存在回归问题。

看到您正在使用MLPClassifier，从而导致“未知标签错误”。尝试使用MLPRegressor。

我无法使用python

1 个答案: