python - 使用 Matplotlib 绘制决策边界时出错

Question

我最近使用 Scikit 模块编写了一个逻辑回归模型。然而，我很难绘制决策边界线。我明确地将系数和截距相乘并绘制它们(这反过来会抛出错误的数字)。

有人能给我指出如何绘制决策边界的正确方向吗？

有没有一种更简单的方法来绘制直线，而无需手动乘以系数和截距？

感谢一百万!

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

#Import Dataset
dataset = pd.read_csv("Students Exam Dataset.txt", names=["Exam 1", "Exam 2", "Admitted"])
print(dataset.head())

#Visualizing Dataset
positive = dataset[dataset["Admitted"] == 1]
negative = dataset[dataset["Admitted"] == 0]

plt.scatter(positive["Exam 1"], positive["Exam 2"], color="blue", marker="o", label="Admitted")
plt.scatter(negative["Exam 1"], negative["Exam 2"], color="red", marker="x", label="Not Admitted")
plt.title("Student Admission Plot")
plt.xlabel("Exam 1")
plt.ylabel("Exam 2")
plt.legend()
plt.plot()
plt.show()

#Preprocessing Data
col = len(dataset.columns)
x = dataset.iloc[:,0:col].values
y = dataset.iloc[:,col-1:col].values
print(f"X Shape: {x.shape}   Y Shape: {y.shape}")

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1306)

#Initialize Model
reg = LogisticRegression()
reg.fit(x_train, y_train)

#Output
predictions = reg.predict(x_test)
accuracy = accuracy_score(y_test, predictions) * 100
coeff = reg.coef_
intercept = reg.intercept_
print(f"Accuracy Score : {accuracy} %")
print(f"Coefficients = {coeff}")
print(f"Intercept Coefficient = {intercept}")

#Visualizing Output
xx = np.linspace(30,100,100)
decision_boundary = (coeff[0,0] * xx + intercept.item()) / coeff[0,1]
plt.scatter(positive["Exam 1"], positive["Exam 2"], color="blue", marker="o", label="Admitted")
plt.scatter(negative["Exam 1"], negative["Exam 2"], color="red", marker="x", label="Not Admitted")
plt.plot(xx, decision_boundary, color="green", label="Decision Boundary")
plt.title("Student Admission Plot")
plt.xlabel("Exam 1")
plt.ylabel("Exam 2")
plt.legend()
plt.show()

数据集:Student Dataset.txt

Answer 1

Is there an easier way to plot the line without having to manually multiply the coefficients and the intercepts?

是的，如果您不需要从头开始构建它，mlxtend 中有一个从 scikit-learn 分类器绘制决策边界的出色实现。包裹。提供的链接中的文档内容丰富，并且可以使用 pip install mlxtend 轻松安装。

首先，关于您发布的代码的 Preprocessing block 的几点:
1. x 不应包含类标签。
2. y 应该是一个 1d 数组。

#Preprocessing Data
col = len(dataset.columns)
x = dataset.iloc[:,0:col-1].values # assumes your labels are always in the final column.
y = dataset.iloc[:,col-1:col].values
y = y.reshape(-1) # convert to 1d

现在绘图就像这样简单:

from mlxtend.plotting import plot_decision_regions
plot_decision_regions(x, y,
                      X_highlight=x_test,
                      clf=reg,
                      legend=2)

此特定图通过包围 x_test 数据点来突出显示它们。