data mining practical machine learning tools and techniques

hyperplane are called support vectors.The following content, let me refreshing.To the top, suddenly there is no mess of things, Lin directly said that this is a typical quadratic programming (QP) problem;Typical features: The most optimized expression is two times, that is, the problem is a conventional routine to solve.How to follow the regular routine of QP to engage? Just sort out a few parameters and it's OK. It seems a little silly to see here: What about the kkt stuff? You're not talking

the FP tree class treenode: def _ init _ (self, namevalue, numoccur, parentnode): Self. name = namevalue self. count = numoccur self. nodelink = none self. parent = parentnode self. children = {} def Inc (self, numoccur): Self. count + = numoccur def disp (self, IND = 1): Print ''' * ind, self. name, '', self. count for child in self. children. values (): child. disp (IND + 1) # load data def loadsimpd At (): simpdat = [['R', 'z', 'h', 'J', 'P'], ['Z

unsupervised learning: Clustering: The process of dividing a data set into multiple classes composed of similar objects Density Analysis: The process of describing statistical values If you select an appropriate algorithm: Selection basis: 1. Use algorithms. 2. Analyze or collect data. Selection process: 1. Select supervised

Python Machine Learning Practical tutorialsShare Network address--https://pan.baidu.com/s/1miib4og Password: WTIWThe course is really good, share to everyoneMachine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory

(classification)Random Forest classifierGradient Elevation Decision TreeLinear regression linearregression SgdregressorSupport Vector Machine regressionWeighted average of K-point arithmetic mean/distance difference in K-nearest neighbor RegressionRegression treeIntegration Model (regression)General Random ForestLift Tree ModelExtreme Random Forest: When constructing a split node of a tree, you do not randomly select features but first collect a subs

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Scikit-learn is a python-based machine learning module based on BSD open source licenses. The project was first initiated by Davidcournapeau in 2007 and is currently being maintained by community volunteers.Scikit-learn's official website is http://scikit-learn.org/stable/, where you can find related Scikit-learn resources, module downloads, documentation, routines and more.Scikit-learn installation require

in fact, Machine Learning has been addressing a variety of important issues. For example , in the mid-decade, people have begun to use neural networks to scan credit card transactions to find fraudulent behavior; at the end of the year,Google Use this technology for Web search. but at that time, machine learning was n

As the beginning of the basic learning of machine learning, record the knowledge and practice choice of machine Learning Foundation.Bibliography:Machine learning CombatAn electronic version of both Chinese and English PDF files an

classification data This part, considering the space is limited, interested in their own can go into the detailed study of other uses , exceptionally powerful.summing up this part, Matlab comes with neural network toolbox compared to the previous section of their own, for linear data accuracy is about the same, but for the division of non-linear data, Toolbox fu

Python Chinese translation-nltk supporting book;2. "Python Text processing with NLTK 2.0 Cookbook", this book to go deeper, will involve NLTK code structure, but also will show how to customize their own corpus and model, etc., quite good Pattern The pattern, produced by the clips Laboratory at the University of Antwerp in Belgium, objectively says that pattern is not just a set of text processing tools, it is a Web

size as the input matrix.>>> Import knn>>> Reload (KNN) Six, the test algorithmone of the most important tasks in machine learning algorithms is to evaluate the correctness of the algorithm, usually we train the classifier with 90% of the existing data, and use the remaining 10% data to test the classifier to detect t

see the distribution is reasonable, but most of the load amount is negative, this problem can be solved laterThe stock index is forecasted by principal component analysis:Market.index 　　To evaluate our predictions, we compare the predicted stock index with the Dow Jones Indices, a well-known stock index.Dji.prices 　　It is noted here that the predictions are "actually negatively correlated", which is also the problem caused by the negative load shown above. This small problem can only be solved

++ = 1.0 currline = line. strip (). split ('\ t') linearr = [] For I in range (21): linearr. append (float (currline [I]) If int (classifyvector (Array (linearr), trainweights ))! = Int (currline [21]): errorcount + = 1 errorrate = (float (errorcount)/numtestvec) print 'the error rate of this test is: % F' % errorrate return errorratedef multitest (): numtests = 10; errorsum = 0.0 for K in range (numtests): errorsum + = colictest () print 'after % d iterations the average error rate is: % F' %

explain 30%, it should be wrong in the book. It also explains why the book mentions that 1% of hasadvertising can be shed without mentioning 3% of Inenglish.Analysis: Since hasadvertising only explains the results of 1%, in practice, if the input is easy to obtain, it is worthwhile to include all inputs into a predictive model, and if it is difficult to obtain, it can be removed from the model#################################Correlation Brief:Correlation can be used to measure the relationship

,:] = Img2vector (' trainingdigits/%s '% filenamestr) testfilelist = Listdir (' testdigits ') #iterate through T He test set errorcount = 0.0 mtest = Len (testfilelist) for I in Range (mtest): Filenamestr = Testfilelist[i ] Filestr = Filenamestr.split ('. ') [0] #take off. txt classnumstr = int (Filestr.split ('_') [0]) Vectorundertest = Img2vector (' testdigits/%s ' % filenamestr) Classifierresult = Classify0 (Vectorundertest, Trainingmat, Hwlabels, 3) print "The Classifie R came back with:%d,

The 2nd Chapter data analysis#machine learing for Heckers#chapter 2Library (GGPLOT2) heights.weights 　　#不同区间宽度的直方图Ggplot (Heights.weights, aes (x = height)) + geom_histogram (binwidth = 1) ggplot (Heights.weights, aes (x = height)) + geom_his Togram (binwidth = 5) ggplot (Heights.weights, aes (x = Height)) + geom_histogram (binwidth = 0.001)　　#密度曲线图Ggplot (Heights.weights, aes (x = Height)) + geom_density (

: matplotlib Annotation Matplotlib provides an annotation tool annotations, which can be used to add text annotations to data graphs. Annotations are usually used to interpret data. I didn't understand this code, so I only gave the code in the book. #-*-Coding: cp936-*-import matplotlib. pyplot as pltdecisionnode = dict (boxstyle = 'sawtooth ', Fc = '0. 8 ') leafnode = dict (boxstyle = 'round4', Fc = '0. 8

-spherical and large-sized variations.The disadvantage of K-means clustering algorithm is that the result is not the global optimal, and the convergence speed of large scale data is slow.the work flow of the K-means algorithm : a bunch of data, select the K initial point as the centroid, for each point in the dataset, find its nearest centroid, assign it to the cluster that the centroid belongs to. Finally,

understand the task, so "save the Earth" to understand "kill all human beings." This is like a typical predictive algorithm that literally understands the task and ignores the other possibilities or the practical significance of the task.So, in January 2016, Harvard Business School professor Michael Luca, professor of economics Sendhil Mullainathan, and Cornell University professor Jon Kleinberg, published an article titled "Algorithm and Butler" in