statistics and machine learning toolbox

\%s.txt'I'R'). Read () forIinchRange (1, 20)] Transpamlist= [Open (r'C:\Users\Administrator\Desktop\machinelearninginaction\Ch04\email\spam\%s.txt'I'R'). Read () forIinchRange (1, 20)] forLineinchtranhamlist:temp. Set_tran_data (line, True) forLineinchtranspamlist:temp. Set_tran_data (line, False) testlist= [Open (r'C:\Users\Administrator\Desktop\machinelearninginaction\Ch04\email\ham\%s.txt'I'R'). Read () forIinchRange (21, 26)] forLineinchtestlist:PrintTemp.classifiy (line)The

. For example, if other conditions are certain, smokers who are at risk of lung cancer are 5 times times more likely to be non-smokers, then if I now know that a person is lung cancer, I would like to ask you whether this person smokes or smokes. How do you judge? You probably don't know anything about this person, and the only thing you've got is that smoking is more prone to lung cancer, so you're guessing this guy doesn't smoke? I believe you are more likely to say that this man smokes. Why?

MySpace qizmt is a mapreduce framework designed to run and develop distributed computing application projects running on Windows Server large-scale clusters. MySpace qizmt is an open-source framework initiated by MySpace to develop trustworthy, scalable, and super-Simple distributed application projects. Open Source Address: http://code.google.com/p/qizmt /. Infer. NET is an open-source framework that runs Bayesian inference in graphical mode. It is also used for ProbabilityProgramDesign. Open

decision tree of machine learning algorithmWhat is a decision treeDecision Trees (decision tree) are simple but widely used classifiers. By training data to build decision tree, the unknown data can be efficiently classified. The decision-making number has two advantages:1 Thedecision tree model can be read well, descriptive, and helpful for manual analysis;2) High efficiency, the decision tree only need to

ADD1 () DROP1 () 9. Regression Diagnostics Does the sample conform to the normal distribution? Normality test: function shapiro.test (X$X1) The distribution of normality Learning set/Is there outliers? How to find Outliers is the linear model reasonable? Maybe the relationship between nature is more complicated. Whether the error satisfies the independence, equal variance (the error is no

], respectively, is defined as:Visually, covariance represents the expectation of the total error of two variables.If the trend of the two variables is the same, that is, if one is greater than the expected value of the other, then the covariance between the two variables is positive, and if the two variables change in the opposite direction, that is, one of the variables is greater than its own expectation, and the other one is less than its own expectation. Then the covariance between the two

example.) the the Data set contains 3 classes of instances each, where each class refers to a the type of iris plant. One class is linearly separable from the other 2; the the latter is not linearly separable from each other. the - References in ---------- the -FISHER,R.A. "The use of multiple measurements in taxonomic problems" the Annual eugenics, 7, part II, 179-188 (1936); also in "Contributions to About mathematical Statistics "(John Wiley, NY,

Advice for students of machine learningWritten by David MimnoOne of my students recently asked me for advice on learning ML. Here's what I wrote. It ' s biased toward my own experience, but should generalize.My Current Favorite Introduction is Kevin Murphy's book (Machine learning). Might also want to look at books by

instrumental permutation test (permutation test) in the use of statistics in RF is used to measure the importance of feature items. n samples, D dimensions per sample, in order to measure the importance of one of the features di, according to permutation test the N sample of the di features are shuffled shuffle, shuffle before and after the error subtraction is the importance of this feature. RF often does not use permutation Test during trai

One of the top ten algorithms for Machine Learning: EM algorithm. One of the top 10, which makes people think Nb-rich. What is Nb? We generally say someone is Nb because he can solve problems that others cannot solve. Why God is God, because God can do things that many people cannot do. So what problems can the EM algorithm solve? Or the reason why the EM algorithm came to this world has attracted so many p

attributed to a class that indicates whether the patient was infected with diabetes within 5 years, by the time the measurement was measured. If yes, then 1, or 0. The standard dataset has been studied several times in the machine learning literature, with a good prediction accuracy of 70%-76%. Here is a sample from the Pima-indians.data.csv file to find out what data we're going to use. Note: Download

Original URL: http://m.blog.csdn.net/article/details?id=49130173 first, transcendental probability, posterior probability, Bayesian formula, likelihood function In machine learning, these concepts are always involved, but never really understand the connection between them. Here's a good idea to start with the basics, Memo. 1. Prior probability A priori probability relies only on subjective empirical esti

build the model.In the exponential distribution family expression of the Bernoulli distribution we have known:, thus obtained.Three assumptions for building a generalized linear model: Assuming that the Bernoulli distribution is met, , in Bernoulli distribution The derivation process is as follows:As with the least squares model, the next work is done by gradient descent or Newton's method.Note the above push to the result, recall, in the logistic regression, we choose th

Machine Learning: how to use the least squares and Python multiplication in python The reason for "using" rather than "Implementing" is that the python-related class library has helped us implement specific algorithms, and we only need to learn how to use them. With the gradual mastery and accumulation of technology, when the algorithms in the class library cannot meet their own needs, we can also try to im

, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the probability of transitions between statesThis is the proba

the maximum age for garbage.If set to 0, then the young generation object does not go through the survivor area, directly into the old generation. For older generations of more applications, can improve efficiency. If this value is set to a larger value, the younger generation objects are duplicated multiple times in the Survivor area, which increases the survival time of the object's younger generations, increasing the introduction of being recycled in the younger generation.Collector Settings

Chisqtestresult object with P-value, Test statistics and degrees of freedom for each feature. Labels and features must be discrete. linear regression Classification and regression, supervised learning, all used to Mllib.regression.LabledPoint class, Lable+freature vectorRefers to the linear combination of features to predict output values, also supports regular regression of L1 and L2, Lasso and Ridge regr

components are 0.5 and 1 respectively) X = [0 0; 1 0; 0 2] D = Pdist (X, ' Seuclidean ', [0.5,1]) Results: D = 2.0000 2.0000 2.8284 6. Markov distance (Mahalanobis Distance) (1) Markov distance definition There are m sample vectors x1~xm, the covariance matrix is denoted as s, the mean values are denoted as vector μ, and the Markov distances of sample vectors x to u are expressed as: Where the Markov distance between the Vector XI and XJ is defined as: If the covariance matrix is a unit mat

nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the prob