Tag：linear

Map implementation principle
1 what is map In the most popular words, map is a data structure that obtains value through key. Its underlying storage method is array. When the key is stored, the key cannot be repeated. When the key is repeated, the value is overwritten. We hash the key (which can be simply understood as converting […]

R language ecology jags simulation data, linear regression, Cormack jolly Seber CJS model fitting MCMC to estimate animal survival and visualization
Original link:http://tecdat.cn/?p=24721 In this paper, I use two examples that may be of interest to population ecologists to illustrate the use of “jags” to simulate data: first, linear regression, and second, to estimate animal survival (formulated as a state space model). Recently, I have been trying to simulate data from complex hierarchical models. I’m using […]

Mathematical modeling algorithm: grey prediction model GM (1,1) and Python code
Grey prediction model GM (1,1) Grey prediction model\(GM(1,1)\)It is a commonly used prediction method in mathematical modeling competitions, and it is often used for medium and shortterm prediction that conforms to the exponential law. Its mathematical expression and principle analysis refer to the web page and literature at the end of the article. Pretreatment The […]

A preliminary study of machine learning linear regression
Digression I have long been very interested in artificial intelligence. I remember my graduation thesis in college, which used genetic algorithm to solve a classic path finding problem.I have always been in awe and worship of classical human ideas, such as traditional computer data structure algorithm problems, such as classical sorting algorithm or dynamic programming […]

This article will give you an intuitive understanding of linear transformation
The author is a master of software engineering. If there are mistakes in the article, please don’t hesitate to comment.This note is only for exchange and study. If you need to reprint it, please indicate the source. 1、 Definition of linear transformation Baidu Encyclopediaaboutlinear transformation The definition of is as follows: Linear mapping is from […]

Brief introduction to the principle of antagonistic attack
Because the input form of machine learning algorithm is a kind of numerical vectors, the attacker will design a targeted numerical vector to make the machine learning model misjudge, which is called adversarial attack. Unlike other attacks, adversarial attacks mainly occur when adversarial data is constructed. The adversarial data is input into the machine learning […]

Supervised learning, summary of common algorithms for unsupervised learning, citing the scikit learn Library (supervision)
Why write this blog Recently, I was exposed to this knowledge, but I found a lot of notes, but I didn’t feel that they were well summarized. It was just like learning and reviewing while walking. Big guy spray lightly. BibliographyBasic course of Python machine learning It will be summarized from the following five aspects […]

Learning artificial intelligence together  nonlinear regression 2
Artificial intelligence has attracted the attention of all walks of life in China. This course is set up for small partners who want to devote themselves to artificial intelligence and big data science. Based on the artificial intelligence system course developed by Microsoft Research Asia, it prepares basic knowledge explanation, cases and code display for […]

What is machine learning regression algorithm? [linear regression, normal equation, gradient descent, regularization, under fitting and over fitting, ridge regression]
1. Linear regression 1.1 application scenario of linear regression House price forecast Sales quota forecast Finance: loan limit prediction, using linear regression and coefficient analysis factors 1.2 what is linear regression 1.2.1 definitions and formulas Linear regression usesRegression equation (function)For one orBetween multiple independent variables (eigenvalues) and dependent variables (target values)An analytical method of modeling […]

R language nonparametric model to determine premium rate: local regression, generalized additive model GAM, spline regression
Original link:http://tecdat.cn/?p=14121 This paper will analyze several smoothing techniques used to formulate insurance premium rates. Premium not broken down The price should be related to the pure premium, which is proportional to the frequency because No covariates, expected frequency should be Deviance Residuals: Min 1Q Median 3Q Max 0.5033 0.3719 0.2588 0.1376 13.2700 Coefficients: Estimate Std. Error z value Pr(>z) (Intercept) 2.6201 0.0228 114.9 <2e16 *** \\\ Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 12680 on 49999 degrees of freedom Residual deviance: 12680 on 49999 degrees of freedom AIC: 16353 Number of Fisher Scoring iterations: 6 > exp(coefficients(regglm0)) […]

Research on traffic casualty accident prediction based on extended tecdatr language Markov transformation model
Original link:http://tecdat.cn/?p=12227 Original source:Tuoduan data tribe official account abstract This paper describes the analysis process of Markov transformation model in R language. Firstly, the simulation data set is modeled in detail. Next, the Markov transformation model is fitted to the real data set with discrete response variables. Different methods used to validate the modeling of […]

R language nonparametric model to determine insurance premium rate: local regression, generalized additive model GAM, spline regression
Original link:http://tecdat.cn/?p=14121 This paper will analyze several smoothing techniques used to formulate insurance premium rates. The premium is not subdivided The price should be related to the pure premium, which is proportional to the frequency because No covariates, expected frequency should be Deviance Residuals: Min 1Q Median 3Q Max 0.5033 0.3719 0.2588 0.1376 13.2700 Coefficients: Estimate Std. Error z value Pr(>z) (Intercept) 2.6201 0.0228 114.9 <2e16 *** \\\ Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 12680 on 49999 degrees of freedom Residual deviance: 12680 on 49999 degrees of freedom AIC: 16353 Number of Fisher Scoring iterations: 6 […]