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1.线性模板和最小平方
·线性回归也可用于简单的分类，boundary虽然简单，但模型势必不准确。
·存在问题：
ESL P13：两种场景
·scikit-learn:
LinearModel.LinearRegression()
class LinearRegression(LinearModel, RegressorMixin): """ Ordinary least squares Linear Regression. Parameters ---------- fit_intercept :（拟合截距） boolean, optional whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize : boolean, optional, default False If True, the regressors X will be normalized before regression. copy_X : boolean, optional, default True If True, X will be copied; else, it may be overwritten. n_jobs : int, optional, default 1 The number of jobs to use for the computation. If -1 all CPUs are used. This will only provide speedup for n_targets > 1 and sufficient large problems. Attributes ---------- coef_ : 系数，斜率。array, shape (n_features, ) or (n_targets, n_features) Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features. intercept_ : 截距。array Independent term in the linear model.
2.最近邻模型
·样本之间的欧式距离
·k-nearest-neighbors:随着k的增加，分类准确率提高，错误率下降；反之亦反，但或造成过拟合。实际上有效的参数是N/k，而非k。下图表现出N/k，k，error之间的关系。
·scikit-learn:
3.二者比较
最小平方：低方差，高偏差；
k-nearest-neighbors：高方差，低偏差。
Least Squares & Nearest Neighbors
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原文地址：http://www.cnblogs.com/JXPITer/p/5244189.html
