from sklearn.datasets import make_regression; #导入线性回归模型: from sklearn.linear_model import LinearRegression; #--- #生成用于回归分析的数据集: X,y=make_regression(n_samples=50,n_features=1,n_informative=1,\ noise=50,random_state=1); reg=LinearRegression() reg.fit(X,y); #--- #...
import numpy as np from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error # 生成一个回归数据集 X, y = make_regression(n_samples=100, n_features=1, noise=10, random_state=42) # 添加截距项 X_b = n...
make_regression方法的返回值类似于make_blobs,区别是第二个输出不是类型,而是值。同时还有一个区别是,当make_regression的coef参数为真时,make_regression的第三个返回值是一个数组,表示用于生成数据的线性模型的参数 实例代码: from sklearn.datasets importmake_regressionimport numpy as np data = make_regression...
sklearn.datasets.make_regression(n_samples=100, n_features=100, n_informative=10, n_targets=1, bias=0.0, effective_rank=None, tail_strength=0.5, noise=0.0, shuffle=True, coef=False, random_state=None)[source] 导入数据-训练模型: from __future__ import print_function from sklearn import ...
from sklearn.datasets import make_moons from matplotlib import pyplot from pandas import DataFrame # generate 2d classification dataset X, y = make_moons(n_samples=100, noise=0.1) # scatter plot, dots colored by class value df = DataFrame(dict(x=X[:,0], y=X[:,1], label=y)) ...
X, y = make_moons(n_samples=100, noise=0.1) 完整的例子如下所示。 fromsklearn.datasetsimportmake_moons frommatplotlibimportpyplot frompandasimportDataFrame # generate 2d classification dataset X, y = make_moons(n_samples=100, noise=0.1) ...
from sklearn.datasets import make_regression X, y = make_regression(n_samples=200, n_features=10, random_state=0, shuffle=False) #定义模型 def model(n_estimators, max_depth,min_samples_leaf): model= RandomForestRegressor(n_estimators=int(n_estimators), ...
from sklearn.metrics import mean_absolute_error from sklearn.model_selection import train_test_split #make_regression 用来生成样本数据,用于回归模型 from sklearn.datasets import make_regression # n_sampless:生成样本个体的数量 #n_features: 特征数量(x的数量) ...
X, y = make_moons(n_samples=100, noise=0.1) 完整的例子如下所示。 fromsklearn.datasetsimportmake_moons frommatplotlibimportpyplot frompandasimportDataFrame # generate 2d classification dataset X, y = make_moons(n_samples=100, noise=0.1) ...
datasets.make_multilabel_classification datasets.make_regression datasets.make_s_curve datasets.make_sparse_coded_signal datasets.make_sparse_spd_matrix datasets.make_sparse_uncorrelated datasets.make_spd_matrix datasets.make_swiss_roll 下面以make_regression()函数为例,首先看看函数语法: ...