Question

我想使用逻辑回归从Excel数据集中预测和绘制curve，并获取其斜率系数。但是，当我运行代码（见下文）时，会出现错误“ ValueError：未知标签类型：'continuous'。”。

我在类似的问题中读到，y值应为'int'类型，但我不想对其进行转换，因为y值在1.66和0.44之间...

是否有针对此类情况的解决方案，还是应该尝试其他回归模型？

非常感谢

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import seaborn as sns
from sklearn.linear_model import LogisticRegression


df = pd.read_excel('Fatigue2.xlsx',sheet_name='Sheet4')

X = df[['Strain1', 'Temperature1']]
y = df['Cycles1']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=101)

#poly = PolynomialFeatures(degree=2)
#X_ = poly.fit_transform(X_train)

LR = LogisticRegression()
LR.fit(X_train,y_train)

g = sns.lmplot(x='Cycles1', y='Strain1', hue = 'Temperature1', data=df, fit_reg= False)
g.set(xscale='log', yscale ='log')
g.set_axis_labels("Cycles (log N)", "Strain")

print ('Coefficients : ', LR.coef_, 'Intercept :', LR.intercept_)

关于数据，我在Excel工作表中总共有97个值：

Cycles1   Strain1    Temperature1

27631     1.66         650
...       ...          650
6496220   0.44         650

Answer 1

LogisticRegression中的

sklearn是一个分类器，即它期望响应变量是分类的。

您的任务是回归。此外，该图似乎没有右侧logit的渐近行为。如here所述，使用多项式回归可能会获得更好的结果。

Answer 2

def type_of_target（y）： “”“确定目标指示的数据类型。

Note that this type is the most specific type that can be inferred.
For example:

    * ``binary`` is more specific but compatible with ``multiclass``.
    * ``multiclass`` of integers is more specific but compatible with
      ``continuous``.
    * ``multilabel-indicator`` is more specific but compatible with
      ``multiclass-multioutput``.

Parameters
----------
y : array-like

Returns
-------
target_type : string
    One of:

    * 'continuous': `y` is an array-like of floats that are not all
      integers, and is 1d or a column vector.

Logistic回归：ValueError：未知标签类型：'continuous'

2 个答案:

将y更改为y.astype（int）