ValueError: Unknown label type: 'continuous' when using clustering + classification models together

Question

我创建了一个聚类模型，尝试使用 Scikit-Learn 的 KMeans 算法根据年收入和支出得分找到不同的客户群。使用它为每个客户返回的聚类值，我尝试使用来自 sklearn.svm 的支持向量分类创建分类模型。然而，当我尝试将新模型拟合到数据集时，我收到一条错误消息：

File "/Users/user/Documents/Machine Learning A-Z Template Folder/Part 4 - Clustering/Section 24 - K-Means Clustering/cluster_and_prediction.py", line 28, in <module>
    classifier.fit(x_train, y_train)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/svm/_base.py", line 149, in fit
    y = self._validate_targets(y)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/svm/_base.py", line 525, in _validate_targets
    check_classification_targets(y)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/utils/multiclass.py", line 169, in check_classification_targets
    raise ValueError("Unknown label type: %r" % y_type)
ValueError: Unknown label type: 'continuous'

我的代码如下

import pandas as pd 
import numpy as np 

# Using relevant columns from dataset
dataset = pd.read_csv('Mall_Customers.csv')
x = dataset.iloc[:, 3:5].values

# Creating model with ideal amount of clusters
kmeans = KMeans(n_clusters=5, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans.fit(x)

predictions = kmeans.predict(x)

# Creating numpy array for feature scaling
predictions = np.array(predictions, dtype=int)
predictions = predictions[:, None]

from sklearn.preprocessing import StandardScaler
sc_x = StandardScaler()
sc_y = StandardScaler()
x = sc_x.fit_transform(x)
predictions = sc_y.fit_transform(predictions)

# Splitting dataset into training and test sets
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, predictions, test_size=.25)

# Creating Support Vector Classification model
from sklearn.svm import SVC
classifier = SVC(kernel='rbf')
classifier.fit(x_train, y_train)

Elbow Model Used for Clustering

Clustering Visualization

.zip file with the dataset(the dataset is called 'Mall_Customers.csv'

我该如何解决这个问题？

Answer 1

由于您想将此作为 5 类的分类问题来解决，因此您应该不对标签使用缩放器；这会将它们转换为分类模型中的连续变量，因此会出现错误。

此外，与问题无关，但正确的方法是仅将你的缩放器拟合到你的训练数据上，然后使用这个拟合缩放器来转换你的测试数据。

因此，这里是必要的更改（在您完成 predictions 变量的设置之后）：

# initial (unscaled) x used here:
x_train, x_test, y_train, y_test = train_test_split(x, predictions, test_size=.25)
sc = StandardScaler()
x_train_scaled = sc.fit_transform(x_train)
x_test_scaled = sc.transform(x_test)

classifier = SVC(kernel='rbf')
classifier.fit(x_train_scaled, y_train) # no scaling for predictions or y_train

也与问题无关，但您应该在使用 k-means 之前 缩放您的 x 数据，即您实际上应该首先缩放您的 x然后执行聚类（将其留作练习，因为它与错误无关）。

ValueError: Unknown label type: 'continuous' when using clustering + classification models together

ValueError: Unknown label type: 'continuous' when using clustering + classification models together

python

machine-learning

svm

k-means

scikit-learn