如何将异常值添加到线性回归数据集？

Question

我正在尝试查看数据集中的异常值如何影响线性回归模型。我遇到的问题是我不完全知道如何将离群值添加到数据集中，我只在网上找到了大量关于如何检测和删除它们的文章。

这是我目前的代码：

import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression


# Generate regression dataset
X, y = make_regression(
    n_samples=1000,
    n_features=1,
    noise=0.0,
    bias=0.0,
    random_state=42,
)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

regressor = LinearRegression()
regressor.fit(X_train, y_train)  # Training the algorithm

y_pred = regressor.predict(X_test)

print("R2 Score:", metrics.r2_score(y_test, y_pred))
print("Mean Absolute Error:", metrics.mean_absolute_error(y_test, y_pred))
print("Mean Squared Error:", metrics.mean_squared_error(y_test, y_pred))
print("Root Mean Squared Error:", np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

plt.scatter(X_test, y_test)
plt.plot(X_test, y_pred, color="red", linewidth=1)
plt.show()

这是输出：

我的问题是如何向这个干净的数据集添加离群值，以便查看离群值对生成的模型的影响？

任何帮助将不胜感激，谢谢！

Answer 1

您可以直接将值添加到 X 和 y。由于斜率足够大，这最终会给你异常值。您可以使用任何您想要的方法。

import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression


# Generate regression dataset
X, y = make_regression(
    n_samples=1000,
    n_features=1,
    noise=0.0,
    bias=0.0,
    random_state=42,
)

for x in range(20):
    X=np.append(X, np.random.choice(X.flatten()))
    y=np.append(y, np.random.choice(y.flatten()))

X = X.reshape(-1,1)
y = y.reshape(-1,1)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

regressor = LinearRegression()
regressor.fit(X_train, y_train)  # Training the algorithm

y_pred = regressor.predict(X_test)

print("R2 Score:", metrics.r2_score(y_test, y_pred))
print("Mean Absolute Error:", metrics.mean_absolute_error(y_test, y_pred))
print("Mean Squared Error:", metrics.mean_squared_error(y_test, y_pred))
print("Root Mean Squared Error:", np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

plt.scatter(X_test, y_test)
plt.plot(X_test, y_pred, color="red", linewidth=1)
plt.show()

如何将异常值添加到线性回归数据集？

How to add outliers to a Linear Regression dataset?

python

numpy

matplotlib

scikit-learn

linear-regression