linear regression book

Linear regression is the most typical regression problem, and its target value has a linear relationship with all the features . Linear regression is similar to logistic regression, whe

Data research tool for solving defects of output and probability functions The 1th part of this article series refers to the missing three elements in the simple linear regression (Linear regression) class. In this article, the author Paul Meagher uses PHP based probability functions to compensate for these flaws, dem

) - - - #3 Standardized processing of training data and test data -Ss_x =Standardscaler () -X_train =ss_x.fit_transform (X_train) inX_test =ss_x.transform (x_test) - toSs_y =Standardscaler () +Y_train = Ss_y.fit_transform (Y_train.reshape (-1, 1)) -Y_test = Ss_y.transform (Y_test.reshape (-1, 1)) the * #4 Training and forecasting using two linear regression models $ #Initialize linearregression

Same point:Both are generalized linear models GLM (generalized linear models) Different points:1. Linear regression requires that the dependent variable (assuming y) is a continuous numeric variable, while the logistic regression requires that the dependent variable is a di

Simple linear regression implemented using PHP (2) data research tool for solving output and probability function defects At the end of Part 1 of this series of articles, we mention three elements missing from the Simple Linear Regression class. In this article, the author Paul Meagher uses PHP-based probability funct

1 Basic Concepts 1) definition Gradient Descent method is to use negative gradient direction to determine the new search direction of each iteration, so that each iteration can reduce the objective function to be optimized gradually . The gradient descent method is the steepest descent method under the 2 norm. A simple form of the steepest descent method is: X (k+1) =x (k)-a*g (k), where a is called the learning rate, which can be a smaller constant. G (k) is the gradient of X (k). The gradient

This article transferred from: http://blog.csdn.net/itplus/article/details/10857843This paper introduces in detail the linear regression and logistic regression, and introduces the principle of linear regression and the principle of logistic

The Elastic network regression algorithm is a regression algorithm for synthesizing lasso regression and ridge regression, which can control the effect of single coefficients by adding L1 regular and L2 regular term in loss function.ImportTensorFlow as TFImportNumPy as NPImportMatplotlib.pyplot as Plt fromSklearnImport