问题背景

用python的sklearn库做逻辑回归模型训练后，用一些数值去预测结果时报错，已知是多项式的逻辑回归模型

用的是二阶的边界函数画出来的抛物线

代码如下

pay1和pay2是用来预测y结果的，函数中的关键θ（用于计算x2）也提取出来了

#边界函数参数获取
theta0 = LR2.intercept_
theta1,theta2,theta3,theta4,theta5 = LR2.coef_[0][0],LR2.coef_[0][1],LR2.coef_[0][2],LR2.coef_[0][3],LR2.coef_[0][4]
print(theta0,theta1,theta2,theta3,theta4,theta5)

参照该图需要输出新的x2

a = theta4
b = theta5*X1_new + theta2
c = theta0+theta1*X1_new+theta3*X1_new*X1_new
x2_new_2 = (-b+np.sqrt(b*b-4*a*c))/(2*a)
print(x2_new_2)

在预测时出错了

# 预测
x_test = np.array([[80,20]])# 第二个模型预测
y_predict = LR2.predict(x_test)
print(y_predict)

ValueError: X has 2 features, but LogisticRegression is expecting 5 features as input.

问题解决

原因是使用二阶函数计算时，其实需要放入的不仅仅是pay1和pay2这两个数这么简单了，除了x1和x2，还要输入x1x1、x2x2、x1*x2这5个数才可以

# 预测
x_test = np.array([[80,20,80*80,20*20,80*20]])# 第二个模型预测
y_predict = LR2.predict(x_test)
print(y_predict)

输出成功

ValueError: X has 2 features, but LogisticRegression is expecting 5 features as input.相关推荐

【TensorFlow】ValueError: Shape must be rank 1 but is rank 0 for ' ’ with input shapes: [].问题
基于TensorFlow训练mnist数据集出现如下错误: 检测代码,发现是偏置设置格式错误导致. 1.错误代码: # 定义权重和偏置 n_input = 784 n_output = 10 weig ...
SVM ValueError: y should be a 1d array, got an array of shape (1, 250) instead. Found input variable
阅读前请看一下:我是一个热衷于记录的人,每次写博客会反复研读,尽量不断提升博客质量.文章设置为仅粉丝可见,是因为写博客确实花了不少精力.希望互相进步谢谢!! 文章目录阅读前请看一下:我是一个热衷于记 ...
Spark LogisticRegression 逻辑回归之建模
导入包 import org.apache.spark.sql.SparkSession import org.apache.spark.sql.Dataset import org.apache.s ...
Bag of Features (BOF)图像检索算法
FROM:http://blog.csdn.net/loadstar_kun/article/details/39450137 1.首先,我们用surf算法生成图像库中每幅图的特征点及描述符. 2.再 ...
ACM-ICPC 2018 徐州赛区网络预赛 Features Track（STL二维map）
Morgana is learning computer vision, and he likes cats, too. One day he wants to find the cat moveme ...
pytorch简单代码实现deep dream图（即CNN特征可视化 features visualization）
本文给出完整代码实现CNN特征的可视化输入图像,也就是简单的deep dream图,有助于更好的理解CNN工作原理,并掌握用梯度上升法生成满足要求输入图像的技术.更清晰美观的deep dream图 ...
Android Kernel Features
2019独角兽企业重金招聘Python工程师标准>>> Android Kernel Features Jump to: navigation, search Contents 1 ...
vSphere 6.5 High Availability New Features – Proactive HA（vSphere 6.5高可用性新功能–主动HA）
vSphere 6.5 High Availability New Features – Proactive HA vSphere 6.5高可用性新功能–主动HA http://www.vmwarea ...
【NVMe2.0b 14-8】Set Features（下篇）
Set Features 5.27.1.13Host Controlled Thermal Management (Feature Identifier 10h), (可选) 5.27.1.14Non ...

ValueError: X has 2 features, but LogisticRegression is expecting 5 features as input.

问题背景

问题解决

ValueError: X has 2 features, but LogisticRegression is expecting 5 features as input.相关推荐

最新文章

热门文章