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  • VARCOV 计算使用 Matlab |fit| 创建的回归的方差-协方差矩阵功能。 该矩阵不能通过 |fit| 直接访问。 函数,虽然它被用于租约平方优化并在文档以及替代拟合算法(如 |nlinfit|)中引用。 该算法不使用主要输入输出...
  • R计算方差膨胀因子(VIF,Variance Inflation Factor)计算并解读VID与共线性(Multicollinearity)的关系实战 目录 R计算方差膨胀因子(VIF,Variance Inflation Factor)计算并解读VID与共线性...

    R计算方差膨胀因子(VIF,Variance Inflation Factor)计算并解读VID与共线性(Multicollinearity)的关系实战

     

    目录

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  • variance:计算方差-源码

    2021-05-26 18:21:14
    计算。 (有偏样本方差)定义为 无偏定义为 其中x_0, x_1,...,x_{N-1}是单个数据值, N是数据集中值的总数。 安装 $ npm install compute-variance 要在浏览器中使用,请使用 。 用法 var variance = require ...
  • As expected, the total funded amount for the loan and the amount of the loan have a high variance inflation factor because they "explain" the same variance within this dataset. We would need to ...

    python信用评分卡(附代码,博主录制)

    https://etav.github.io/python/vif_factor_python.html

    Colinearity is the state where two variables are highly correlated and contain similiar information about the variance within a given dataset. To detect colinearity among variables, simply create a correlation matrix and find variables with large absolute values. In R use the corr function and in python this can by accomplished by using numpy's corrcoeffunction.

    Multicolinearity on the other hand is more troublesome to detect because it emerges when three or more variables, which are highly correlated, are included within a model. To make matters worst multicolinearity can emerge even when isolated pairs of variables are not colinear.

    A common R function used for testing regression assumptions and specifically multicolinearity is "VIF()" and unlike many statistical concepts, its formula is straightforward:

    $$ V.I.F. = 1 / (1 - R^2). $$

    The Variance Inflation Factor (VIF) is a measure of colinearity among predictor variables within a multiple regression. It is calculated by taking the the ratio of the variance of all a given model's betas divide by the variane of a single beta if it were fit alone.

    Steps for Implementing VIF

    1. Run a multiple regression.
    2. Calculate the VIF factors.
    3. Inspect the factors for each predictor variable, if the VIF is between 5-10, multicolinearity is likely present and you should consider dropping the variable.
    #Imports
    import pandas as pd
    import numpy as np from patsy import dmatrices import statsmodels.api as sm from statsmodels.stats.outliers_influence import variance_inflation_factor df = pd.read_csv('loan.csv') df.dropna() df = df._get_numeric_data() #drop non-numeric cols df.head() 
     idmember_idloan_amntfunded_amntfunded_amnt_invint_rateinstallmentannual_incdtidelinq_2yrs...total_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12m
    0107750112965995000.05000.04975.010.65162.8724000.027.650.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    1107743013141672500.02500.02500.015.2759.8330000.01.000.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    2107717513135242400.02400.02400.015.9684.3312252.08.720.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    31076863127717810000.010000.010000.013.49339.3149200.020.000.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
    4107535813117483000.03000.03000.012.6967.7980000.017.940.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

    5 rows × 51 columns

    df = df[['annual_inc','loan_amnt', 'funded_amnt','annual_inc','dti']].dropna() #subset the dataframe 

    Step 1: Run a multiple regression

    %%capture
    #gather features
    features = "+".join(df.columns - ["annual_inc"]) # get y and X dataframes based on this regression: y, X = dmatrices('annual_inc ~' + features, df, return_type='dataframe') 

    Step 2: Calculate VIF Factors

    # For each X, calculate VIF and save in dataframe
    vif = pd.DataFrame() vif["VIF Factor"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])] vif["features"] = X.columns 

    Step 3: Inspect VIF Factors

    vif.round(1) 
     VIF Factorfeatures
    05.1Intercept
    11.0dti
    2678.4funded_amnt
    3678.4loan_amnt

    As expected, the total funded amount for the loan and the amount of the loan have a high variance inflation factor because they "explain" the same variance within this dataset. We would need to discard one of these variables before moving on to model building or risk building a model with high multicolinearity.

     

    https://study.163.com/course/courseMain.htm?courseId=1005988013&share=2&shareId=400000000398149

    转载于:https://www.cnblogs.com/webRobot/p/10889525.html

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  • Variance

    2021-04-22 12:44:35
    varVarianceSyntaxy = var(X)y = var(X,1)y = var(X,W)y = var(X,W,DIM)ArgumentsXFinancial time series object.WWeight vector used in calculating variance.DIMDimension of X used in calculatingvariance.Desc...

    var

    Variance

    Syntax

    y = var(X)

    y = var(X,1)

    y = var(X,W)

    y = var(X,W,DIM)

    Arguments

    XFinancial time series object.

    WWeight vector used in calculating variance.

    DIMDimension of X used in calculating

    variance.

    Description

    var supports financial time series objects based on the MATLAB®

    var function. See var.

    y = var(X), if X is a financial time series

    object and returns the variance of each series.

    var normalizes y by N –

    1 if N > 1, where

    N is the sample size. This is an unbiased estimator of the

    variance of the population from which X is drawn, as long as

    X consists of independent, identically distributed samples. For

    N = 1, y is normalized by

    N.

    y = var(X,1) normalizes by N and produces the

    second moment of the sample about its mean. var(X, 0) is the same as

    var(X).

    y = var(X,W) computes the variance using the weight vector

    W. The length of W must equal the length of

    the dimension over which var operates, and its elements must be

    nonnegative. var normalizes W to sum to

    1. Use a value of 0 for W

    to use the default normalization by N – 1, or use

    a value of 1 to use N.

    y = var(X,W,DIM) takes the variance along the dimension

    DIM of X.

    Examples

    The variance is the square of the standard deviation. Consider if

    f = fints((today:today+1)', [4 -2 1; 9 5 7])

    Warning: FINTS will be removed in a future release. Use TIMETABLE instead.

    > In fints (line 165)

    Warning: FINTS will be removed in a future release. Use TIMETABLE instead.

    > In fints/display (line 66)

    f =

    desc: (none)

    freq: Unknown (0)

    'dates: (2)' 'series1: (2)' 'series2: (2)' 'series3: (2)'

    '02-Oct-2017' [ 4] [ -2] [ 1]

    '03-Oct-2017' [ 9] [ 5] [ 7]

    then

    var(f, 0, 1)

    is

    Warning: FINTS will be removed in a future release. Use TIMETABLE instead.

    > In fints/var (line 49)

    [12.5 24.5 18.0]

    and

    var(f, 0, 2)

    is

    Warning: FINTS will be removed in a future release. Use TIMETABLE instead.

    > In fints/var (line 49)

    [9.0; 4.0]

    Introduced before R2006a

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  • Bias 和 Variance计算

    万次阅读 2018-09-12 11:41:12
    Bias(偏差)描述的是预期值偏离真实值的大小,所以high bias代表Underfitting(欠拟合)。...下面介绍Bias和Variance计算。 Bias 估计量的bias定义为: 如果,则说估计量是无偏差的。 Bernou...

    Bias(偏差)描述的是预期值偏离真实值的大小,所以high bias代表Underfitting(欠拟合)。
    Variance(方差)描述的是任何特殊采样数据可能造成的与预期值的偏离,所以high variance 代表Overfitting(过拟合)。
    下面介绍Bias和Variance的计算。

    Bias

    估计量的bias定义为:
    bias
    如果无偏差,则说估计量是无偏差的。

    Bernoulli分布的bias计算:
    假设分布期望值是theta,则对于每一个样本xi,分布函数为:
    分布函数1
    计算方法如下图所示:
    伯努利bias
    由上图可得结果是0,所以估计量hattheta是unbiased。

    Gaussian分布
    假设样本服从高斯分布高斯分布
    数学期望估计量的偏差计算方法下图所示:
    高斯分布数学期望
    所以高斯分布的数学期望估计量是无偏差的。
    方差的估计量计算方法如下图所示:
    高斯分布方差
    所以按这种方法啊高斯分布的方差估计量是有偏差的,可以通过设置:
    无偏差形式
    来使高斯分布的方差估计量是无偏差。
    Variance

    方差估计的是随数据采样变化函数的变化情况,估计量的方差写成:
    variance
    Bernoulli分布方差估计量的计算方法
    方差

    展开全文
  • 计算机视觉Github开源论文
  • 计算您的回报,将它们放入excel。 让 Matlab 读取它并运行该文件。 等等。
  • 简单的代码,用于高效地计算移动/滚动或运行方差的在线更新,并使用韦尔福德方法进行平均。 该脚本的目的是逐步计算非常快的滚动方差和均值。 这允许在线处理传入数据。 它使用递归算法来计算当前标准和均值的在线...
  • 实现方差variance计算实现方差variance计算的完整源码(实现,main函数测试) 实现方差variance计算的完整源码(实现,main函数测试) #include <math.h> #include <stdio.h> #include <stdlib.h>...
  • 计算 期望与方差(mean and Variance)在Tensorflow 与 Numpy 对比 Tensorlfow 里 计算期望与方差是用 tf.nn.moments(img, axis) 函数 Numpy 里 计算期望与方差是用 mean 与var 函数 分别计算的。 代码实现如下。 ...
  • numpy 和 pandas 中方差的计算

    万次阅读 2018-04-14 00:37:35
    numpy 中计算的方差就是样本方差本身,公式为: σ2=∑i=1N(xi−x⎯⎯⎯)Nσ2=∑i=1N(xi−x¯)N \sigma^2 = \frac{ \sum\limits_{i=1}^{N}(x_i - \overline x) } { N } pandas 中计算的方差为无偏样本方差,公式为...
  • 回归模型是机器学习中很重要的一类模型,不同于常见的分类模型,回归模型的性能评价指标跟分类模型也相差很大,这里简单基于工作中的一点实践来记录一下基于sklearn库计算回归模型中常用的四大评价指标主要包括:...
  • turf.variance(polygons, points, inField, outField) 计算一组 Polygon|polygons 中一组 Point|points 的字段的方差值。 参数 范围 类型 描述 polygons FeatureCollection.<多边形> 输入多边形 points ...
  • MATLAB中均值、方差、均方差的计算方法
  • 计算参数为lambda的分布的。 lambda可以是number , array ,typed array或matrix 。 var matrix = require ( 'dstructs-matrix' ) , data , mat , out , i ; out = variance ( 2 ) ; // returns ~2.000 ...
  • PCA的方法explained_variance_ratio_计算了每个特征方差贡献率,所有总和为1,explained_variance_为方差值,通过合理使用这两个参数可以画出方差贡献率图或者方差值图,便于观察PCA降维最佳值。 PCA
  • variance和variation的区别

    千次阅读 2019-10-05 17:30:24
    Variance -方差 方差就是一组数据中平均值与任意点之间的距离。 The Variance is the distance between the mean of a set of data to any point in the data. Variation -差异 正常预期结果与观测结果之间...
  • sklearn期望方差explained_variance_score

    千次阅读 2021-04-14 19:57:48
    当期望值(预测值)与真实值相同时,explained_variance_score=1 所以explained_variance_score越小,预测值越远。 发现这个点的起因是,按照sklearn官网例子练习时,突发奇想,测试一下某个回归模型的准确率。但是...
  • Vector 计算 均值(mean) 和 方差(variance)本文地址:http://blog.csdn.net/caroline_wendy/article/details/24623187vector<>类型的数组, 计算均值和方差的最简方法.代码:double sum = std::accumulate(std::...
  • 除了上文提到的概率分布外,还...方差 Variance 标准差 Standard deviation 协方差 Covariance 相关系数 Correlation 协方差矩阵 Covariance matrix 期望 Expectation 函数f(x)f(x)f(x)关于某分布P(x)P(x)...
  • 回归和偏差方差 通过观察相对于拟合(多项式的阶数)的偏差和方差,对数据集执行多项式回归。
  • 理解variability vs. volatility vs. variance

    千次阅读 2020-10-20 21:20:44
    variability In statistics, dispersion (also called variability, scatter, or spread) is the ...Common examples of measures of statistical dispersion are the variance, standard deviation, and interquart..
  • PCA(explained_variance_ratio_与explained_variance_)

    万次阅读 多人点赞 2018-09-09 15:21:12
    这里提一点:pca的方法explained_variance_ratio_计算了每个特征方差贡献率,所有总和为1,explained_variance_为方差值,通过合理使用这两个参数可以画出方差贡献率图或者方差值图,便于观察PCA降维最佳值。...
  • C++ - Vector 计算 均值(mean) 和 方差(variance)Vector 计算 均值(mean) 和 方差(variance)本文地址:http://blog.csdn.net/caroline_wendy/article/details/24623187vector<>类型的数组, 计算均值和方差的最...
  • 偏差(Bias)与方差(Variance)详解

    千次阅读 2020-05-02 10:05:20
     NFL(No Free Lunch Theorem)告诉我们选择算法应当与具体问题相匹配,通常我们看一个算法的好坏就是看其泛化性能,但是对于一个算法为什么好为什么坏,我们缺乏一下认识,”Bias-Variance-Decomposition”就是从偏差,...
  • 本文价值与收获 看完本文后,您将能够作出下面的界面 基础知识 方差是在概率论和统计方差衡量随机变量或一组数据时离散程度的度量...SwiftUI 计算数组方差Variance import SwiftUI struct ContentView: View { @Sta

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