Original: http://blog.csdn.net/abcjennifer/article/details/7700772This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression,

1. Multiple features (multidimensional features)
In the linear regression we mentioned in the single-variable linear regression (linear regression with one variable) of machine learnin

Only one independent variable and the linear regression of the dependent variable are called simple linear regression, but in fact, such a simple relationship in the real world almost does not exist, all things are interconnected, a problem must be produced by a number of factors combined effect of the results.For

. Therefore, here we use RSS to calculate the deviation and obtain a system of m + 1 equations, so that we can obtain the equal value, the corresponding linear regression equation is obtained.
2. r regression modeling
A built-in data set Swiss is provided in R, which is based on various factors that affect the national economy in Switzerland in 1888. For details

http://blog.csdn.net/pipisorry/article/details/43529845Machine learning machines Learning-andrew NG Courses Study notesMultivariate linear regression multivariable linear programming(linear regression works with multiple variables

regression.
The root number can also be selected based on the actual situation.Regular Equation
In addition to Iteration Methods, linear algebra can be used to directly calculate $ \ matrix {\ Theta} $.
For example, four groups of property price forecasts:
Least Squares
$ \ Theta = (\ matrix {x} ^ t \ matrix {x}) ^ {-1} \ matrix {x} ^ t \ matrix {y} $Gradient Descent, advantages and disadvantages of regul

Machine Learning: Linear Regression With Multiple Variables, linearregressionMachine Learning: Linear Regression With Multiple Variables
Next, the example of the previous prediction of the house price leads to a multi-variable

); theta = Theta-alpha (alpha_i) .*grad; end plot (0:49, Jtheta ( 1:50), char (Plotstyle (alpha_i)), ' LineWidth ', 2)% be sure to convert by Char function hold on if (1 = = Alpha (alpha_i))% The experiment found that Alpha was1 o'clock the effect is best, then the theta value after the iteration is the desired value theta_grad_descent = theta endendlegend (' 0.01 ', ' 0.03 ', ' 0.1 ', ' 0.3 ', ' 1 ', ' 1.3 '); Xlabel (' Number of iterations ') ylabel (' cost function ')% below is the pred

input values are all added together to get the predicted values.1, definition of regressionThe simplest definition of regression is to give a point set D, to fit the point set with a function, and to minimize the error between the point set and the fitted function, if the function curve is a straight line, it is called linear regression, and if the curve is a tw

: Attention to diversity and multiple times is two different concepts. "Multivariate" refers to the equation has a plurality of parameters. "Multiple" refers to the highest power of the number of parameters in the equation. The multivariate linear equation is if the predicted value Y and the sample all eigenvalues conform to a multivariate one-time

This article introduces the concepts of fitting and under-fitting, and introduces local weighted regression algorithms.Over fitting and under fittingBefore in linear regression, we always put the individual x as our characteristic, but in fact we can consider that even the higher times of x as our characteristics, then we will get through

1 multivariate linear regression model 1 multivariate regression model and regression equation
Multivariate regression model:y=β0 +β1 x 1 +β2 x 2 +...+βk x k +εMultivariate regression equation:Multiple

. But if N and P are closer, they are prone to overfitting, and if n(2) The problem of model interpretation capability includes many variables in a multivariate linear regression model which may be independent of the response variable, and may produce multiple collinearity phenomena: that is, there is a significant correlation between

called classification problem.Linear regressionSuppose the price is not only related to the area, but also to the number of bedrooms, as follows:At this time \ (x\) is a 2-dimensional vector \ (\in \mathbb{r^2}\). where \ (x_1^{(i)}\) represents the house area of the first ( i\) sample,\ (x_2^{(i)}\) represents the number of house bedrooms for the first \ (i\) sample.We now decide to approximate y as the linear function of x, which is the following f

1 What is linear regressionThe relationship between the dependent variable and several independent variables is determined, and the linear relation model is constructed to predict the dependent variable2 Linear regression principleLeast squares OLS (ordinary learst squares)The minimum squared error between the Y and th

Linear regression learning notes and regression learning notes
Operating System: CentOS7.3.1611 _ x64
Python version: 2.7.5
Sklearn version: 0.18.2
Tensorflow version: 1.2.1
Linear regression is a statistical analysis method that uses r

-validation with the minimum MSE as the Criterion. Usage scenarios:The ORTHOGONALMATCHINGPURSUITCV class is usually used for feature selection of sparse regression coefficients, and this is similar to LASSOCV. however, due to its loss function optimization method is the forward selection algorithm, the accuracy is low, the general situation is not particularly recommended, with LASSOCV is enough, unless you are concerned about the exact number of s

.
From a learning point of view, simple linear regression modeling is worth studying, because it is the only way to understand more advanced forms of statistical modelling. For example, many of the core concepts in simple linear regression have established a good foundation for understanding

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