machine learning algorithms cheat sheet

Machine learning is undoubtedly an important content in the field of data analysis now, people who engage in it work are in the usual work or manyor less will use machine learning algorithms.There are many algorithms for machine

characteristics of the learning, so that the classification is not allowed, for example, a special feature as a category of judging criteria, so that does not have a particular attribute of the data into this category. This is called fitting, English is called overfitting literal translation is over-matching, that is, matching is too thin, a bit too. To solve this problem, it is necessary to simplify the decision tree, to remove some of the character

Machine learning can be divided into several types according to different computational results. These different purposes determine that machine learning can be divided into different models and classifications in practical applications.As mentioned earlier , machine

. 7.5 910.5 . 13.5]]# n Powers of each element of the matrix: n=2mymatrix1 = Mat ([[[1,2,3],[4,5,6],[7,8,9]])print power (mymatrix1,2 1 4 9] [[49 6481]]# matrix multiplied by matrix mymatrix1 = Mat ([[1,2,3],[4,5,6],[7,8,9 = Mat ([[[1],[2],[3]])print mymatrix1*mymatrix2 output: [[[][+][50]]# Transpose of the matrix mymatrix1 = Mat ([[[1,2,3],[4,5,6],[7,8,9]])print mymatrix1. The transpose of the # Matrix to the transpose of the T # Matrix print mymatrix1 output results as follow

Tags: basic machine learning Continue with the original algorithm: (5) Bayesian Method Bayesian algorithms are a class of algorithms based on Bayesian theorem. They are mainly used to solve classification and Regression Problems. Common algorithms include Naive Bayes, a

Tags: basic machine learning Based on the similarity of functions and forms of algorithms, we can classify algorithms, such as tree-based algorithms and neural network-based algorithms. Of course, the scope of

Transferred from: http://www.dataguru.cn/article-10174-1.html Gradient descent algorithm is a very extensive optimization algorithm used in machine learning, and it is also the most commonly used optimization method in many machine learning algorithms. Almost ev

hierarchical approach. So the clustering algorithm tries to find the intrinsic structure of the data in order to classify the data in the most common way. Common clustering algorithms include the K-means algorithm and the desired maximization algorithm (expectation maximization, EM). (8) Association Rules Learning Association rule Learning finds useful associa