multinomial logistic regression interpretation

Most of this series is from the Standford public class machine learning Andrew Teacher's explanation, add some of their own understanding, programming implementation and learning notes.Chapter I. Logistic regression1. Logistic regressionLogistic regression is a kind of supervised learning classification algorithm, compared with the previous linear

Logistic regression (Logistic regression) is a common machine learning method used in the industry to estimate the possibility of something. For example, a user may buy a product, a patient may suffer from a disease, and an advertisement may be clicked by the user. (Note: "possibility", not the "probability" in mathema

: In a nutshell, one may choose the solver with the following rules: Case Solver Small DataSet or L1 penalty "Liblinear" Multinomial loss or large dataset "Lbfgs", "sag" or "NEWTON-CG" Very Large DataSet "Sag" From the above description, we may feel that, since NEWTON-CG, LBFGS and sag so many restrictions, if not a large sample, we choose Liblinear not on the line. Wr

solution, intuitively, can think of, the smallest error expression form. is still a linear model with unknown parameters, a pile of observational data, the model with the smallest error in the data, the sum of the squares of the model and the data is minimal:This is the source of the loss function. Next, is the method to solve this function, there are least squares, gradient descent method.http://zh.wikipedia.org/wiki/%E7%BA%BF%E6%80%A7%E6%96%B9%E7%A8%8B%E7%BB%84Least squaresis a straightforwar

for linear regression, logistic regression, and general regression"Turn from": Http://www.cnblogs.com/jerryleadJerryleadFebruary 27, 2011As a machine learning beginner, the understanding is limited, the expression also has many mistakes, hope that everybody criticizes correct.1 SummaryThis report is a summary and under

Understanding of linear regression, logistic regression and general regression"Please specify the source when reproduced": Http://www.cnblogs.com/jerryleadJerryleadFebruary 27, 2011As a machine learning beginner, the understanding is limited, the expression also has many mistakes, hope that everybody criticizes correct

Original: http://www.cnblogs.com/jerrylead/archive/2011/03/05/1971867.html#3281650Understanding of linear regression, logistic regression and general regression"Please specify the source when reproduced": Http://www.cnblogs.com/jerryleadJerryleadFebruary 27, 2011As a machine learning beginner, the understanding is limi

As a machine learning beginner, the understanding is limited, the expression also has many mistakes, hope that everybody criticizes correct. 1 Summary This report is a summary and understanding of the first four sections of the Stanford University Machine learning program plus the accompanying handouts. The first four sections mainly describe the regression problem, and regression is a method of supervised

intuitive interpretation is as follows: Given an initial point 0 Span style= "Display:inline-block; width:0px; Height:2.183em; " > If F ( θ 0 ) and its derivative of the same number indicates that 0 points on the left side of the initial point, otherwise at the initial point to the right, the initial point of updating the store's tangent of over 0 points to continue the above steps, the resultin

0. Overview The linear regression can not only be used to deal with the regression problem, but also can be converted to the classification by comparison with the threshold value , but the output range of the assumed function is not limited. Such a large output is classified as 1, and a smaller number is divided into 1, which is odd. The output range of the hypothetical function of

event and the probability of non-occurrence. Use p to indicate the probability that the event occurred: odds = p/(1-p).OR: ratio, The probability of event occurrence in the experimental group (odds1)/control group (odds2).Interpretation of the results of three logistic regressionWe use an example to illustrate that this example contains 200 student data, including 1 independent variables and 4 arguments:De

has been heard of logistic regression logistic regression, such as Dr. Wu in the "beauty of mathematics" mentioned that Google is the use of logistic regression to predict the click-through of search ads. Because I have been inter

of estimating and testing the equation.The logistic model is the probability prediction model of occurrence.Initial attempt to modelFor the specific interpretation of the results between P172. Some difficulty, good understanding.Building the final modelFinally, 3 variables (diastolic pressure, hormones, ln platelets) were introduced to establish the final logistic