statistics and machine learning toolbox

ensure reversible ( reversible Sufficient condition : matrix X columns linearly independent )In retrospect, our approach is to use iterative methods to find out the value of the cost function, and not to find the cost function. That is to say, whether the so-called optimal solution can be obtained, either by iteration or by other means, in line with the above conditions.But the reality of the data is not so ideal.If not reversible, how to solve?1, to seek pseudo-inverse (

Spark sreaming and Mllib machine learningOriginally this article is prepared for 5.15 more, but the last week has been busy visa and work, no time to postpone, now finally have time to write learning Spark last part of the content.第10-11 is mainly about spark streaming and Mllib. We know that Spark is doing a good job of working with data offline, so how does it behave on real-time data? In actual productio

Summary of machine learning problems Category Name Keywords Supervised Classification Decision tree Information Gain Classification regression tree Gini index, Gini 2 Statistics, pruning Naive Bayes Non-parameter estimation, Bayesian Estimation Linear Discriminant Analysis Fishre identification, fe

is, the distribution statistics of the numbers appear, and are the result of normalization to the 0~1 interval. That is, the horizontal axis represents the number, and the vertical is the percentage of the number that corresponds to the horizontal axis in the 1000 random numbers. If you do not use the normalized horizontal axis for numbers (Normed=false), the vertical axis indicates the number of occurrences. If normalization is not used--the

In the previous section, we introduced the overall framework of supervised learning and the basic points, according to the total number of thinking, then we will introduce the corresponding algorithms. Today, let's take a look at the application of Bayesian theorem in machine learning. The main points of this chapter are:1. Bayes theorem;2. Bayes theorem in class

Statement:Machine learning series mainly records their own learning machine learning algorithms in the process of some references and summaries, including some of the content is reference books and reference blog.Directory: What are association rules The concepts that must be known in association rules

distance/edit distance), Jaccard Distance (Jaccard distance), Correlation coefficientdistance (correlation coefficient distance), information Entropy (information entropy), KL ( Kullback-leiblerdivergence KL divergence/relative Entropy relative entropy).Optimization (optimized):non-constrained Optimization (unconstrained optimization): Cyclic Variable Methods (variable rotation method), Pattern search Methods (pattern searching method), Variable Simplex Methods (variable simplex method), Gradie

Nine algorithms for machine learning---regressionTransferred from: http://blog.csdn.net/xiaohai1232/article/details/59551240Regression analysis is to quantify the size of the dependent variable affected by the independent variable, to establish a linear regression equation or a nonlinear regression equation, so as to predict the dependent variable, or the interpretation of the dependent variable.The regress

paper is usually European-style distance, Pearson coefficient or cosine similarity.Assuming that a matrix A is established, the M*n matrix, the rows are all users, n is all items, each element of the matrix represents the user's rating of the item, then the item-based or user-based recommendation is to calculate the similarity of all columns or all rows. In real life, this matrix is very sparse.Topic: Recommend users to buy TOPN itemsThe Matrix C is a m*n matrix, each row represents each user,

Brief introductionMost of the text classification methods use model-based classification, which can be divided into two main categories: 1 based on the rule classification method, the classification rules are determined for each category of the class set, then the text is classified according to the category template, and the category of the text is determined. The rules based text classification methods include: Decision tree, association rule and Rough set, etc. 2 based on the statistical clas

BoostingBoosting in training will give a weight to the sample, and then make the loss function as far as possible to consider those sub-error class samples (such as to the sub-class of the weight of the sample to increase the value)Convex optimizationThe optimal value of a function is often solved in machine learning, but in general, the optimal value of any function is difficult to solve, but the glo

special value of 0, because 0 does not affect the value update of the LR classifier.The partial deletion of sample eigenvalues in training data is a tricky issue, and many documents are devoted to solving the problem, as it is too bad to lose the data directly, and the cost of re-acquisition is expensive. Some optional data loss processing methods include:-Use the mean value of the available features to fill the missing values;-use special values to ± true complement missing values, such as-1;-

finite but large quantities of t instead; second, using the bootstrapping method in statistics To generate new data based on existing data simulations.bootstrappingThe data sampled by Bootstrap is randomly averaged out in the original n data, recorded and then re-extracted, and then taken n times, the resulting data is statistically referred to as Bootstrap sample.BaggingThe method of bootstrap aggregation (BAGging) is to generate a series of differe

That years. I learn the main contents of machine learning:1. Basic introduction to machine learning, getting started with machine learning; 2. Linear regression and logistic. XX Performance Prediction System. Intelligent interacti

http://www.zhihu.com/question/20822481 know the user,non-paper, non-rationaleSpirit_dongdong,wildog,MT practices and others agree Agree @ Zhang Ziquan, add a little bit more. Look at the problem estimates, the subject may be Learning machine learning things, so there will be this problem. But as other people have pointed out, the two approaches are not quite com

samples from n samples that have been put back2. Set up a classifier on the full attribute of the N samples (cart,svm)3, repeat the above steps, the establishment of a m classifier4, the prediction of the use of voting methods to obtain resultsBoostingBoosting in training will give a weight to the sample, and then make the loss function as far as possible to consider those Sub-error class samples (such as to the sub-class of the weight of the sample to increase the Value)Convex optimizationThe

equal to 1.5789 (greater than 1 is not related, since this is the value of the density function and is used only to reflect the relative probability of each value).With this data, the gender classification can be calculated. P (Height =6| male) x p (weight =130| male) x P (foot Palm =8| male) x p (male)= 6.1984 x e-9 P (Height =6| female) x p (weight =130| female) x P (foot Palm =8| female) x P (female)= 5.3778 x e-4 It can be seen that the probability of a woman is nearly 10,

Datasets: Exposing datasets100+ interesting data sets for statistical data http://rs.io/100-interesting-data-sets-for-statistics/Data Set subreddit https://www.reddit.com/r/datasetsUCI Machine Learning Library http://archive.ics.uci.edu/ml/ information : From a personal bloghttp://www.cnblogs.com/hellochennan/p/5352110.htmlhttp://www.cnblogs.com/hellochenn

Similarity measurement in machine learning, Comparison of method summaryai lin 1 weeks ago (01-10) 876 ℃ 0 Reviews CangwuWhen classifying, it is often necessary to estimate the similarity metric between different samples (similarity measurement), which is usually done by calculating the "distance" (Distance) between samples. The method used to calculate the distance is very fastidious, even related to the

Documenting today's exploration of machine learning directions, the Unit's laboratory environment is comfortable to use. Praise.Record my every step in the field of machine learning to grow. This experimental material was taken from Mr. Lin Dague's Big Data analysis and machine