首先从SKLearn工具库的linear_model中引入LinearRegression;创建模型对象命名为model,设置超参数normalize为True(在每个特征值上做标准化,这样能保证拟合的稳定性,加速模型拟合速度)。 from sklearn.linear_model import LinearRegression model = LinearRegression(normalize=True) model 1. 2. 3. 4. 5. 输出: Linear...
from sklearnimportlinear_model 其次训练线性回归模型。使用 fit() 喂入训练数据,如下所示: 代码语言:javascript 复制 model=linear_model.LinearRegression()线性回归 model.fit(x,y) 最后一步就是对训练好的模型进行预测。调用 predict() 预测输出结果, “x_”为输入测试数据,如下所示: 代码语言:javascript 复...
This class implements regularized logistic regression using the 'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. **Note that regularization is applied by default**. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats...
10 # complete documentation. 11 ---> 12 from .base import LinearRegression 13 14 from .bayes import BayesianRidge, ARDRegression C:\WinPython-32bit-2.7.5.1\python-2.7.5\lib\site-packages\sklearn\linear_model\base.py in <module>() 28 from ..utils.sparsefuncs import (csc_mean_variance_a...
# sklearn linear_model linearRegression # sklearn.linear_model.LinearRegression -scikit-learn0.23.2 documentation # 参数:class sklearn.linear_model.LinearRegression(*, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None) # fit_intercept:是否计算此模型的截距,默认启用,如果设置为false,则...
from sklearn.linear_model import LinearRegression reg = LinearRegression() reg.fit(xxx,xxxx) print(reg.predict([27)) #查看预测 print(reg.coef_) #查看斜率 print(reg.intercept_) #查看截距,即R平方数+ print(reg.score(xxx, xxx)) #查看准确率 1 2 3 4 5 6 7 8清理异常值的步骤 初次...
http://scikit-learn.org/stable/documentation.html 官方文档 https://www.jianshu.com/p/c6bfc9052325 前言 于sklearn的使用来说,目前只是想成为一名调包侠,但是调包侠起码也得知道有哪些包可以调,为此找了一些教程想快速入门,通过一番查找,终于找到了一名大佬叫做莫烦写的教程,教程短小实用,对我想快速了解sklearn...
(1)线性回归 linear regression (2)局部加权回归Locally weighted regression (3)逻辑回归 logistic Regression (4)逐步回归 stepwise regression (5)岭回归 Ridge Regression (6)Least Absolute Shrinkage and Selection Operator ( LASSO ) (7)弹性网络 Elastic Net (L1+L2) ...
Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression extra_warning...
# Modifiedfordocumentation by Jaques Grobler # License:BSD3clauseimportnumpyasnpimportmatplotlib.pyplotasplt from sklearn.linear_modelimportLogisticRegression from sklearnimportdatasets #importsome data to playwithiris=datasets.load_iris()X=iris.data[:,:2]# we only take the first two features.Y=ir...