我遇到了一个问题，我正在尝试为当地报纸创建一个推荐系统(作为学校项目)，但是当我尝试使用model_selection库中的cross_validate功能时遇到了麻烦。
我正在尝试使用 SVD 并获得 f1 分数。但我有点困惑。所以这是无监督学习，我没有测试集，所以我想使用 KFolding 进行交叉验证。我相信这个的折叠数由cross_validate函数中的"cv"参数表示。这是对的吗？
当我尝试运行代码时出现问题，因为我得到以下堆栈跟踪：https://hastebin.com/kidoqaquci.tex
我没有向cross_validate函数的"y"参数传递任何东西，但这错了吗？这不是测试集应该去的地方吗？正如我所说，我没有任何测试集，因为这是无人监督的。我在这里查看了第 3.1.1.1 章中的示例： http://scikit-learn.org/stable/modules/cross_validation.html
看起来他们正在cross_validate函数中传递数据集的"目标"。但是为什么它们同时传递目标集和 cv 参数？cv 值大于 1 是否表示应使用折叠，并且遗漏的折叠将用作目标(测试)集？
还是我完全误解了什么？为什么我在堆栈跟踪中收到"缺少参数"错误？
这是失败的代码：
from sklearn.model_selection import cross_val_score as cv
from sklearn.decomposition.truncated_svd import TruncatedSVD
import pandas as pd
# keywords_data_filename = 'keywords_data.txt'
active_data_filename = 'active_time_data.txt'
header = ['user_id', 'item_id', 'rating']
# keywords_data = pd.read_csv(keywords_data_filename, sep='*', names=header, engine='python')
active_time_data = pd.read_csv(active_data_filename, sep='*', names=header, engine='python')
# Number of users in current set
print('Number of unique users in current data-set', active_time_data.user_id.unique().shape[0])
print('Number of unique articles in current data-set', active_time_data.item_id.unique().shape[0])
# SVD allows us to look at our input matrix as a product of three smaller matrices; U, Z and V.
# In short this will help us discover concepts from the original input matrix,
# (subsets of users that like subsets of items)
# Note that use of SVD is not strictly restricted to user-item matrices
# https://www.youtube.com/watch?v=P5mlg91as1c
algorithm = TruncatedSVD()
# Finally we run our cross validation in n folds, where n is denoted by the cv parameter.
# Verbose can be adjusted by an integer to determine level of verbosity.
# We pass in our SVD algorithm as the estimator used to fit the data.
# X is our data set that we want to fit.
# Since our estimator (The SVD algorithm), We must either define our own estimator, or we can simply define how it
# score the fitting.
# Since we currently evaluate the enjoyment of our users per article highly binary, (Please see the rate_article fn in
# the filter script), we can easily decide our precision and recall based on whether or not our prediction exactly
# matches the binary rating field in the test set.
# This, the F1 scoring metric seems an intuitive choice for measuring our success, as it provides a balanced score
# based on the two.
cv(estimator=algorithm, X=active_time_data, scoring='f1', cv=5, verbose=True)
这里有多个问题：
1)截断SVD是一种降维算法。所以我不明白你打算如何计算f1_score。
2)f1_score传统上用于分类任务，并有一个公式：
f1 = 2*recall*precision
--------------------
recall + precision
其中召回率和精度是根据真阳性、真阴性、假阳性、假阴性定义的，这反过来又需要计算真实类和预测类。
3) cv = 1 毫无意义。在cross_val_score中，cv表示折叠数。所以 cv = 5 表示在每次折叠中，80% 的数据将在训练中，20% 的数据将在测试中。那么，您打算如何在没有某种真实真实标签的情况下测试数据。