Linear Regression 线性回归方法是机器学习里面最基础的一种方法,相关的理论方面的知识有很多,这里就不介绍了,博客主要从scikit-learn库的使用方面来探讨算法。 首先,我们使用随机生成一组数据,然后加入一些随机噪声。 View Code 首先,不添加正则项 View Code 误差为: 当多项式的最高次幂超过6之后,训练样本
(3) 如果是对 one-hot 转换后的每个特征构建列联表进行卡方检验,那将会是个巨大的工程,因为one-hot 转换通常会使特征维数成倍增加。因此我们需要一个快速计算 \(\chi^2\) 的方法,而不是繁琐地对每个特征计算列联表频数,所幸scikit-learn中就提供了这样的快捷方法,同时也将看到这个方法也为连续型变量的应用...
This is a demo or practice about how to use Simple-Linear-Regression in scikit-learn with python. Following is the package version that I use below: The Python version: 3.6.2 The Numpy version: 1.8.0rc1 The Scikit-Learn version: 0.19.0 The Matplotlib version: 2.0.2 Training Data Here ...
fromsklearn.pipelineimportPipeline # 加载线性回归模型 fromsklearn.linear_modelimportLinearRegression # 构建线性回归模型 pipe_lm=Pipeline([ ('lm_regr',LinearRegression(fit_intercept=True)) ]) # 训练线性回归模型 pipe_lm.fit(x_train,y_train) # 使用线性回归模型进行预测 y_train_predict=pipe_lm.pr...
同样的,我们需要对数据进行标准化处理。这里使用scikit-learn里提供的StandardScaler。 fromsklearn.preprocessingimportStandardScaler# Standardize the data (mean=0, std=1) using training dataX_scaler=StandardScaler().fit(X_train)y_scaler=StandardScaler().fit(y_train)# Apply scaler on training and test da...
如何找到每个系数的 p 值(显着性)? 这有点矫枉过正,但让我们试一试。首先让我们使用 statsmodel 找出 p 值应该是什么 import pandas as pd import numpy as np from sklearn import datasets, linear_model from sklearn.linear_model import LinearRegression ...
Performing linear regression using Scikit-Learn: from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X, y) lin_reg.intercept_, lin_reg.coef_ lin_reg.predict(X_new) based on thescipy.linalg.lstsq()(the name stands for "least squares") ...
Simple Linear Regression with Sklearn To demonstrate simple linear regression using the sklearn library, we'll use a California house price prediction dataset from Kaggle. Importing Libraries import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import Lin...
This lesson is the first of a two-part lesson focusing on an indispensable set of data analysis methods, logistic and linear regression. It provides an overview of linear regression and walks through running both algorithms in Python (using scikit-learn). The lesson also discusses interpret...
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