pyspark linear regression

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, regularization, neural network, design of the

Ufldl Study Notes and programming assignments: Linear Regression (linear regression) Ufldl provides a new tutorial, which is better than the previous one. Starting from the basics, the system is clear and has programming practices. In the high-quality deep learning group, you can learn DL directly without having to de

Linear regression is prone to problems of fitting or less fitting.Local weighted linear regression is a non-parametric learning method, when the new samples are predicted, the new weights are re-trained, and the values of the parameters are obtained by retraining the sample data, each time the parameter value of the pr

1 linear regression algorithmHttp://www.cnblogs.com/wangxin37/p/8297988.htmlThe term regression refers to the fact that we predict an accurate output value based on the previous data, for this example is the price, and there is another most common way to supervise learning, called classification, when we want to predict discrete output values, for example, we are

This series is from the Standford public class machine learning Andrew Teacher's explanation, attaching some of their own programming implementation and learning notes.The first chapter Linear regression1. Linear regressionLinear regression is a method of supervised learning.The main idea of linear

Tomorrow the first class 8.55 only, or the things you see today to tidy up.Today is mainly to see Ng in the first few chapters of the single-line regression, multi-linear regression, logistic regression of the MATLAB implementation, before thought those things understand well, but write code is very difficult to look,

Use tensorflow to implement linear regression of data Import related libraries import tensorflow as tfimport numpyimport matplotlib.pyplot as pltrng = numpy.random Parameter settings learning_rate = 0.01training_epochs = 1000display_step = 50 Training data train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167, 7.042,10.791,5.313,7.997,5.654,9.27,3.1])train_Y = numpy

1-Questions raised 2-Linear regression 3-Theoretical derivation 4-python/spark implementation1 #-*-coding:utf-8-*-2 fromPysparkImportSparkcontext3 4 5theta =[0, 0]6Alpha = 0.0017 8sc = Sparkcontext ('Local')9 Ten deffunc_theta_x (x): One returnSUM ([i * j forI, JinchZip (theta, X)]) A - defCost (x): -thx =func_theta_x (x) the returnThx-x[-1] - - defPartial_theta (x): -DIF =Cost

Classification and logistic regression (classification and logistic regression)Http://www.cnblogs.com/czdbest/p/5768467.htmlGeneralized linear model (generalized Linear Models)Http://www.cnblogs.com/czdbest/p/5769326.htmlGenerate Learning Algorithm (generative learning algorithms)Http://www.cnblogs.com/czdbest/p/577150

The simple linear regression model is described earlier, followed by the multiple linear regression model.Simple linear regression is a linear regr