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python - sklearn出现错误（LogisticRegression模型选择）

问题描述

    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    from sklearn import datasets
    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import LogisticRegression
    from sklearn.preprocessing import StandardScaler


    Dt = pd.read_csv("D:\wisc_bc_data.csv")
    '''
    print(Dt.shape)     
    print(Dt.head())
    '''
     def changer(x):
         if x == 'B':
            return 0
         else:
            return 1
     Dt['diagnosis'] = Dt['diagnosis'].map(lambda x: changer(x))
     features = Dt[2:12]
     Diagnosis = Dt['diagnosis']
     train_features, test_features, train_labels, test_labels = train_test_split(features, Diagnosis) 'this line emits error code'

     '''
     this is my code and i used dataset from here: https://gomguard.tistory.com/52
     '''

我想拆分数据以进行逻辑回归。但是，出现了这样的错误代码：

----> 1 train_features，test_features，train_labels，test_labels = train_test_split(features，Diagnosis)中的ValueError Traceback（最近一次调用）

D:\python\lib\site-packages\sklearn\model_selection_split.py in train_test_split(*arrays, **options) 2116 raise TypeError("Invalid parameters passed: %s" % str(options)) 2117 -> 2118 arrays =可索引（*数组）2119 2120 n_samples = _num_samples（数组 [0]）

D:\python\lib\site-packages\sklearn\utils\validation.py in indexable(*iterables) 246 """ 247 结果 = [_make_indexable(X) for X in iterables] --> 248 check_consistent_length(*result) 249 返回结果 250

D:\python\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays) 210 if len(uniques) > 1: 211 raise ValueError("Found input variables with distinct numbers of" --> 212 “样本：%r”% [int(l) for l in length]) 213 214

ValueError: Found input variables with contrast numbers of samples: [10, 569] 我该如何解决？

标签： pythonpandasscikit-learnlogistic-regression

解决方案

我认为features = Dt[2:12]导致你的错误。您的尝试是对特征进行切片，但 python 将代码解释为切片记录。因此，将代码更改为Dt.iloc[:, 2:12]。

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