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KNN的K值选取

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note：近似误差：可以理解为对现有训练集的训练误差。估计误差：可以理解为对测试集的测试误差。如果选择较小的K值，就相当于用较小的领域中的训练实例进行预测，“学习”近似误差会减小，只有与输入实例较近或相似的训练实例才会对预测结果起作用，与此同时带来的问题是“学习”的估计误差会增大，换句话说， K值的减小就意味着整体模型变得复杂，容易发生过拟合；如果选择较大的K值，就相当于用较大领域中的训练实例进行预测，其优点是可以减少学习的估计误差，但缺点是学习的近似误差会增大。这时候，与输入实例较远（不相似的）训练实例也会对预测器作用，使预测发生错误，且K值的增大就意味着整体的模型变得简单。在实际应用中， K值一般取一个比较小的数值，例如采用交叉验证法（简单来说，就是一部分样本做训练集，一部分做测试集）来选择最优的K值。来源： CSDN 作者： Jellyqin 链接： https://blog.csdn.net/tengchengtu4139/article/details/103465747

kNN进邻算法

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一、算法概述（1）采用测量不同特征值之间的距离方法进行分类优点：精度高、对异常值不敏感、无数据输入假定。缺点：计算复杂度高、空间复杂度高。（2）KNN模型的三个要素 kNN算法模型实际上就是对特征空间的的划分。模型有三个基本要素：距离度量、K值的选择和分类决策规则的决定。距离度量距离定义为： L p ( x i , x j ) = ( ∑ l = 1 n | x ( l ) i − x ( l ) j | p ) 1 p Lp(xi,xj)=(∑l=1n|xi(l)−xj(l)|p)1p 一般使用欧式距离：p = 2的个情况 L p ( x i , x j ) = ( ∑ l = 1 n | x ( l ) i − x ( l ) j | 2 ) 1 2 Lp(xi,xj)=(∑l=1n|xi(l)−xj(l)|2)12 K值的选择一般根据经验选择，需要多次选择对比才可以选择一个比较合适的K值。如果K值太小，会导致模型太复杂，容易产生过拟合现象，并且对噪声点非常敏感。如果K值太大，模型太过简单，忽略的大部分有用信息，也是不可取的。分类决策规则一般采用多数表决规则，通俗点说就是在这K个类别中，哪种类别最后就判别为哪种类型二、实施kNN算法 2.1 伪代码计算法已经类别数据集中的点与当前点之间的距离按照距离递增次序排序选取与但前点距离最小的k个点

How to get the most contributing feature in any classifier Sklearn for example DecisionTreeClassifier knn etc

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问题 I have tried my model on a data set using KNN classifier , I would like to know which is the most contributing feature in the model, and most contributing feature in the prediction. 回答1: To gain qualitative insight into which feature has greater impact on classification you could perform n_feats classifications using one single feature at a time ( n_feats stands for the feature vector dimension), like this: import numpy as np from sklearn import datasets from sklearn.neighbors import

Q: KNN in R — strange behavior

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问题 Does anyone know why the following KNN R code gives different predictions for different seeds? This is strange as K<-5, and thus the majority is well defined. In addition, the floating numbers are not that small to fall under a precision of data problem. (remark: I know the test is weirdly different from the training. This is only a synthetic example created to demonstrate the strange KNN behavior) library(class) train <- rbind( c(0.0626015, 0.0530052, 0.0530052, 0.0496676, 0.0530052, 0

python实现kNN算法识别手写体数字

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1。总体概要 kNN算法已经在上一篇博客中说明。对于要处理手写体数字，需要处理的点主要包括：（1）图片的预处理：将png，jpg等格式的图片转换成文本数据，本博客的思想是，利用图片的rgb16进制编码（255，255，255）为白色，（0，0，0）为黑色，获取图片大小后，逐个像素进行判断分析，当此像素为空白时，在文本数据中使用0来替换，反之使用1来替换。 from PIL import Image '''将图片转换成文档，使用0，1分别替代空白和数字''' pic = Image.open( '/Users/wangxingfan/Desktop/1.png' ) path = open( '/Users/wangxingfan/Desktop/1.txt' , 'a' ) width = pic.size[ 0 ] height = pic.size[ 1 ] for i in range( 0 ,width): for j in range( 0 ,height): c_RGB = pic.getpixel((i,j)) #获取该像素所对应的RGB值 if c_RGB[ 0 ]+c_RGB[ 1 ]+c_RGB[ 2 ]> 0 : #白色 path.write( '0' ) elif c_RGB[ 0 ]+c_RGB[ 1 ]+c_RGB[ 2 ]== 0 : #黑色

What's the difference between ANN, SVM and KNN classifiers?

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问题 I know this is a very general question without specifics about my actual project, but my question is: I am doing remote sensing image classification. I am using the object-oriented method: first I segmented the image to different regions, then I extract the features from regions such as color, shape and texture. The number of all features in a region may be 30 and commonly there are 2000 regions in all, and I will choose 5 classes with 15 samples for every class. In summary: Sample data 1530

KNN with class weights in SKLearn [closed]

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问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 7 months ago . Is it possible to define class weights for a K-nearest neighbour classifier in SKLearn? I have looked at the API but cannot work it out. I have a knn problem which has very imbalanced numbers of classes (10000 of some, to 1 of others). 回答1: The original knn in sklearn does not seem to offer that option. You can

OpenCV3中的机器学习算法

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OpenCV3中加入了几种机器学习算法，可以将机器学习算法与图像和视频处理结合起来。可参考： OpenCV/OpenCV3计算机视觉软件支持库和最新资源 OpenCV3的最新特征 OpenCV3的人脸检测－使用Python OpenCV3的机器学习算法kNN－使用Python OpenCV3的kNN算法进行OCR识别－使用Python OpenCV3的机器学习算法SVM－使用Python OpenCV3的机器学习算法-K-means－使用Python 来源： oschina 链接： https://my.oschina.net/u/2306127/blog/626538

Unique assignment of closest points between two tables

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问题 In my Postgres 9.5 database with PostGis 2.2.0 installed, I have two tables with geometric data (points) and I want to assign points from one table to the points from the other table, but I don't want a buildings.gid to be assigned twice. As soon as one buildings.gid is assigned, it should not be assigned to another pvanlagen.buildid . Table definitions buildings : CREATE TABLE public.buildings ( gid numeric NOT NULL DEFAULT nextval('buildings_gid_seq'::regclass), osm_id character varying(11)

How to plot a ROC curve for a knn model

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问题 I am using ROCR package and i was wondering how can one plot a ROC curve for knn model in R? Is there any way to plot it all with this package? I don't know how to use the prediction function of ROCR for knn. Here's my example, i am using isolet dataset from UCI repository where i renamed the class attribute as y: cl<-factor(isolet_training$y) knn_isolet<-knn(isolet_training, isolet_testing, cl, k=2, prob=TRUE) Now my question is, what are the arguments to pass to the prediction function of

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