/
githubmirror
/
scikit-learn
Обзор
Документация
Войти
/
githubmirror
/
scikit-learn
Код
Запросы
0
Пакеты
0
Релизы
0
Аналитика
Безопасность
main
examples/neighbors/plot_regression.py
52 строки
2 KB
Drew Craeton
DOC Add link to plot_regression.py (#29232)
08 июл 2024, 15:21
Не верифицирован
08 июл 2024, 15:21
1e3c7be
Код
Авторство
О чём код?
""" ============================ Nearest Neighbors regression ============================ Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpolation of the target using both barycenter and constant weights. """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause # %% # Generate sample data # -------------------- # Here we generate a few data points to use to train the model. We also generate # data in the whole range of the training data to visualize how the model would # react in that whole region. import matplotlib.pyplot as plt import numpy as np from sklearn import neighbors rng = np.random.RandomState(0) X_train = np.sort(5 * rng.rand(40, 1), axis=0) X_test = np.linspace(0, 5, 500)[:, np.newaxis] y = np.sin(X_train).ravel() # Add noise to targets y[::5] += 1 * (0.5 - np.random.rand(8)) # %% # Fit regression model # -------------------- # Here we train a model and visualize how `uniform` and `distance` # weights in prediction effect predicted values. n_neighbors = 5 for i, weights in enumerate(["uniform", "distance"]): knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights) y_ = knn.fit(X_train, y).predict(X_test) plt.subplot(2, 1, i + 1) plt.scatter(X_train, y, color="darkorange", label="data") plt.plot(X_test, y_, color="navy", label="prediction") plt.axis("tight") plt.legend() plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights)) plt.tight_layout() plt.show()