rstudio regression

Now machine learning algorithms in classification, regression, data mining and other issues on the use of a very broad, for beginners, may be heard ' algorithm ' or other exclusive nouns feel inscrutable, so many people are deterred, which makes many people in dealing with a lot of problems lost a very useful tool. Machine learning algorithm is not so advanced, here I use the most popular language to explain the meaning of the expression of the algori

the basic framework of machine learning: Model, target (cost function), optimization algorithm STEP1: For a problem, we need to first establish a model, such as regression or classification model; Step2: The cost function of the model is established by the minimum classification error, maximum likelihood or maximum posterior probability; Step3: Solving the optimization problem A. If there is an analytic solution to the optimization function, it is pos

Linear regression is introduced earlier, but in reality, it is unrealistic to use linear regression to fit the entire dataset. In reality, data is often not globally linear.Of course, we also introduced local weighted linear regression, which has some limitations. Here we will introduce another idea, tree regression.The basic idea is to use a decision tree to di

This paper mainly explains the local weighted (linear) regression. Before explaining the local weighted linear regression, we first explain two concepts: under-fitting, over-fitting, and the local weighted linear regression algorithm.Under fitting, over fittingsuch as the three fit models. The first one is a linear model, the training data fitting is not good eno

http://blog.csdn.net/ppn029012/article/details/8908104 Machine Learning---2. From maximum likelihood to view linear regression classification: Mathematics machine Study 2013-05-10 00:34 3672 people read comments (15) Collection Report MLE machine learning Directory (?) [+] From maximum likelihood again see linear regression 1. Review of linear regression In the

First, the cognition and application scenario of logistic regression Logistic regression is a probabilistic nonlinear regression model, which is a study of the relationship between two classification observations and some influencing factors. A multi-variable analysis method. The usual problem is to study whether a certain outcome occurs in some conditions, such

I was excited when I saw this part of the content, because it was finally linked to the theoretical content of my previous studies, which is part of the code implementation of the previous logistic regression theory, so if something that is not quite understood can be returned to the theoretical part to understand, Below we enter the topic----Logistic regressionFirst, sigmoid functionIn the previous theoretical part we know that if we need to classify

1. Background knowledge In just the end of the cat Big Data S1 competition, logic regression is common use and the effect of a good algorithm. (1) return Let's say what is regression, for example, we have two types of data, each with 500 points, when we draw these points, there will be a line between the two sets of data, we draw this curve (because it is very likely non-linear), is

GLM Generalized linear model George Box said: "All models is wrong, some is useful" 1. Starting with the Linear Model As a foundation of GLM, this section review the classic Linear Regression, and expounds some basic terms.The basic formula for our linear regression is essentially to look at x x and then predict Y y with a simple linear function h (x) H (x):Y=h (x) =wtx y=h (x) =w^tx 1.1 dependent variable

Softmax regression is a generalized version of the logistic regression.But the logistic regression is a 2 classification problem, and Softmax is a multi-classification problem, that's all.The Softmax regression was degenerate into a logistic regression in the case of 2 classification.The cost function of logistic regre

In understanding the principle of logistic regression algorithm, we point out the definition of the loss function of logistic regression (here we re-contract the symbol):For a single sample, the desired output of the sample is denoted as Y, and the actual output of the sample is recorded as Y_hat, then the loss function of the logistic regression can be expressed

Original: http://blog.csdn.net/qll125596718/article/details/8248249In supervised learning, if the predicted variable is discrete, we call it classification (e.g. decision tree, support vector machine, etc.), if the predicted variable is continuous, we call it regression. In regression analysis, if you include only one argument and one dependent variable, and the relationship between the two can be approxima