ruby machine learning

one, factor decomposition machineFMthe Modelfactor decomposition Machine (factorization machine, FM) is bySteffen Rendlea machine learning algorithm based on matrix decomposition is proposed. 1, Factor decomposition machineFMThe advantagesfor factor decomposition machinesFM, the most important feature is that the spars

neighbor point, and then can establish a neighbor map, so calculate the distance between two points of the problem, The transition becomes the shortest path problem (Dijkstra algorithm) between two points on the nearest neighbor graph.So what is the ISOMAP algorithm? In fact, it is a variant of the MDS algorithm, the same idea as the MDS, but in the calculation of the distance of the high-dimensional space is the geodesic distance, rather than the real expression of the European distance betwee

perhaps this loss function is quite in line with the characteristics of SVM ~Multi-Classification problemMethod One:As shown--each time a category is taken out, other categories are synthesized into a large category, which is treated as a two classification problem. Repeat n times to be OKCons: The category of the line will be biased to the training data of the smaller categoryMethod Two: Simultaneous requestExplain the formula:The left is a point of classification at J XJ multiplied by its own

Machine Learning-multiple linear regression and machine Linear Regression What is multivariate linear regression? In linear regression analysis, if there are two or more independent variablesMultivariable linear regression). If we want to predict the price of a house, the factors that affect the price may include area, number of bedrooms, number of floors, and ag

The stronger the fault tolerance, the better.B is the plane's biased forward, W is the plane's normal vector, and the X-to-plane mapping:First of all, the point is the smallest distance from the dividing line, and then ask what kind of W and B, so that the point, the value of the distance dividing line is the largest.After shrinking:and taking it as min, take yi* (W^t*q (xi) + b) = 1 =Machine Learning algor

above question, we can apply the kernel function:Quadratic coefficient q n,m = y n y m z n T z m = y n y m K (x N, x m) to get the Matrix Qd.So, we need not to de the caculation in space of Z, but we could use KERNEL FUNCTION to get znt*zm used xn and XM.Kernel Trick:plug in efficient Kernel function to avoid dependence on d?So if we give the This method a name called Kernel SVM:Let us come back to the 2nd polynomial, if we add some factor into expansion equation, we may get some new kernel fun

is still published as a reading note, not involving too many code and tools, as an understanding of the article to introduce machine learning.The article is divided into two parts, machine learning Overview and Scikit-learn Brief Introduction, the two parts of close relationship, combined writing, so that the overall length, divided into 1, 22.First, it's about

take an average of this evaluation mode.It is a useful algorithm to use the F-score algorithm to evaluate both precision and recall rates . The PR of the molecule determines that the precision ratio (P) and recall (R) must be large at the same time to ensure that the F score values are larger. If the precision ratio or recall rate is very low, close to 0, the direct result of the PR value is very low, approaching 0, that is, F score is also very low.At this point we compare three algorithms, we

Machine learning Types Machine Learning Model Evaluation steps Deep Learning data Preparation Feature Engineering Over fitting General process for solving machine learning