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第680行： − + + − {{Main|Regression analysis}}+ − − Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is [[linear regression]], where a single line is drawn to best fit the given data according to a mathematical criterion such as [[ordinary least squares]]. The latter is often extended by [[regularization (mathematics)]] methods to mitigate overfitting and bias, as in [[ridge regression]]. When dealing with non-linear problems, go-to models include [[polynomial regression]] (for example, used for trendline fitting in Microsoft Excel <ref>{{cite web|last1=Stevenson|first1=Christopher|title=Tutorial: Polynomial Regression in Excel|url=https://facultystaff.richmond.edu/~cstevens/301/Excel4.html|website=facultystaff.richmond.edu|accessdate=22 January 2017}}</ref>), [[Logistic regression]] (often used in [[statistical classification]]) or even [[kernel regression]], which introduces non-linearity by taking advantage of the [[kernel trick]] to implicitly map input variables to higher dimensional space. − − Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel ), Logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher dimensional space. − − '''回归分析 Regression Analysis'''包含了大量的统计方法来估计输入变量和它们的相关特征之间的关系。它最常见的形式是'''线性回归 Linear Regression'''，根据一个数学标准，比如一般最小平方法，画一条线来最好地拟合给定的数据。后者通常通过正则化(数学)方法来扩展，以减少过拟合和偏差，如岭回归。在处理非线性问题时，常用的模型包括多项式回归(例如，在 Microsoft Excel 中用于趋势线拟合)、 Logit模型回归（通常用于分类）甚至核回归，它利用核技巧将输入变量隐式地映射到更高维度空间，从而引入了非线性。
→回归分析 Regression analysis
[[Image:Linear regression.svg|thumb|upright=1.3|Illustration of linear regression on a data set.数据集上的线性回归]]
[[Image:Linear regression.svg|thumb|upright=1.3|Illustration of linear regression on a data set.数据集上的线性回归]]
==== 回归分析 Regression analysis ====
==== 回归分析 ====
'''回归分析 Regression Analysis'''包含了大量的统计方法来估计输入变量和它们的相关特征之间的关系。它最常见的形式是'''线性回归 Linear Regression'''，根据一个数学标准，比如一般最小平方法，画一条线来最好地拟合给定的数据。后者通常通过正则化(数学)方法来扩展，以减少过拟合和偏差，如岭回归。在处理非线性问题时，常用的模型包括多项式回归(例如，在 Microsoft Excel 中用于趋势线拟合<ref>{{cite web|last1=Stevenson|first1=Christopher|title=Tutorial: Polynomial Regression in Excel|url=https://facultystaff.richmond.edu/~cstevens/301/Excel4.html|website=facultystaff.richmond.edu|accessdate=22 January 2017}}</ref>)、 Logit模型回归（通常用于分类）甚至核回归，它利用核技巧将输入变量隐式地映射到更高维度空间，从而引入了非线性。
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==== 贝叶斯网络 Bayesian networks ====
==== 贝叶斯网络 Bayesian networks ====