KNN Regression

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准备数据

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_regression
from sklearn.neighbors import KNeighborsRegressor

X, y = make_regression(n_samples=200, n_features=1, n_informative=1, noise=20, random_state=1)
# (200, 1)
X.shape
# (200,)
y.shape

plt.figure(figsize=(12, 8))
plt.scatter(X, y, c='r')

建模训练

# KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
#                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,
#                     weights='uniform')
knn = KNeighborsRegressor(n_neighbors=5)
knn.fit(X, y)

评价模型

# 0.9544385303760471
knn.score(X, y)

X_test = np.linspace(-2, 2, 500).reshape(-1, 1)
y_test = knn.predict(X_test)
# (500, 1)
X_test.shape
# (500,)
y_test.shape

plt.figure(figsize=(12, 8))
plt.scatter(X, y, c='r', s=100, label='samples')
plt.plot(X_test, y_test, c='g', linewidth=3, label='prediction')
plt.legend()

Reference

https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html

https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html