most common machine learning algorithms

Machine Learning common algorithm subtotalsMachine learning is undoubtedly a hot topic in the field of current data analysis. Many people use machine learning algorithms more or less in

') plt.ylabel (' Ratio_sugar ') plt.title (' LDA ') plt.show () W=calulate_w () plot (W)The results are as follows: The corresponding W value is:[ -6.62487509e-04, -9.36728168e-01]Because of the relationship between data distribution, LDA's effect is not obvious. So I changed the number of samples of several label=0, rerun the program to get the result as follows:The result is obvious, the corresponding W value is:[-0.60311161,-0.67601433]Transferred from: http://cache.baiducontent.com/c?m= 9d7

Perception Machine: This is the simplest machine learning algorithm, but there are a few points to note. The first is the selection of the loss function, and in order to minimize the loss function, the gradient descent method used in the iterative process, finally obtains the optimal w,bThe visual interpretation is to adjust the value of the w,b, so that the sepa

In the process of machine learning, we often meet the problem of fitting. The high dimension of input data or features is one of the problems that lead to overfitting. The higher the dimension, the more sparse your data will be in each feature dimension, which is basically catastrophic for machine learning

classification method is used to solve the nonlinear problem in two steps, first using a transform to map the data of the original space to the new space, and then using the line-line classification learning method in the new space.Learn the classification model from the training data.If a kernel function is semi-positive, it is valid.In order to solve the problem of outliers, penalties are introduced. The new model should not only make the interval

. Or after the derivation of the formula can not be interpreted, or the number of unknown parameters is greater than the number of equations. At this point, the iterative algorithm is used to find the optimal solution step-after-step. In particular, if the optimization function is a convex function, then there is a global optimal solution, if the function is non-convex, then there will be many local optimal solutions, so the importance of convex optimization is self-evident. People always wan

]) *double (Dy[i])#Sqx = double (Dx[i]) **2Sumxy= VDOT (Dx,dy)#returns the point multiplication of two vectors multiplySQX = SUM (Power (dx,2))#Square of the vector: (x-meanx) ^2#calculate slope and interceptA = sumxy/SQXB= meany-a*MeanxPrintA, b#Draw a graphicPlotscatter (XMAT,YMAT,A,B,PLT)7.1.4 Normal Equation Group methodCode implementation of 7.1.5 normal equation set#data Matrix, category labelsXarr,yarr = Loaddataset ("Regdataset.txt")#Importing Data Filesm= Len (Xarr)#generate x-coordinat

"Dry" machine learning common algorithm subtotals2015-07-21 Big Data Digest Big Data DigestBig Data DigestNumber Bigdatadigestfunction Introduction Data make the financial, Internet, it changes and subvert the medical, agricultural, catering, real estate, transportation, education, manufacturing and even human itself. To popularize data thinking and disseminate d

threshold of the class, and it is saved for clustering. This method of finding EPs mainly takes into account that data sets of different densities should be based on the density of each data. The appropriate thresholds were selected for clustering. Because the parameters used in clustering can only determine the density difference in the same class of data in the cluster results, the error caused by the parameter selection will not have a great effect on the clustering result.2.2 DBSCAN cluster

: KneighborsclassifierCommon: KNEIGHBORSCLASSIFIER:KNN nearest neighbor algorithm, nearestneighbors: Nearest neighbor algorithm, kneighborsregressor:k nearest neighbor algorithm, nearestcentroid: Nearest centroid algorithm4. Logistic regression algorithm: logisticregression5. Stochastic forest algorithm, random Forest Classifier:randomforestclassfierOne of the most commonly used: Randomforestclassifier: Random forest algorithm, baggingcclassifier:bagging bagging algorithm6. Decision Tree algorit

This section learns to use Sklearn for voting classification, see a specific example, the dataset uses the Iris DataSet, using only the sepal width and petal length two dimension features, Category we also only use two categories: Iris-versicolor and Iris-virginica, the standard uses ROC AUC.Python Machine learning Chinese catalog (http://www.aibbt.com/a/20787.html)Reprint please specify the source, Python

Read Catalogue 1. Learning Style 1.1 Supervised learning 1.2 Non-supervised learning 1.3 Semi-supervised learning 1.4 Intensive Learning 2. Algorithm classification 2.1 Regression algorithm 2.2 Instance-based

Supervised learningKNN k Nearest Neighbor algorithmdef classify0 (InX, DataSet, labels, k):Datasetsize = dataset.shape[0]Diffmat = Tile (InX, (datasetsize,1))-DataSetSqdiffmat = diffmat**2Sqdistances = Sqdiffmat.sum (Axis=1)distances = sqdistances**0.5classcount={}For I in range (k):Voteilabel = Labels[sorteddistindicies[i]]Classcount[voteilabel] = Classcount.get (voteilabel,0) + 1Sortedclasscount = sorted (Classcount.iteritems (), Key=operator.itemgetter (1), reverse=true)return sortedclasscoun

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 algorit

The idea of clustering: dividing a DataSet into several subsets (called a cluster cluster) that you don't want to cross, each potentially corresponding to a concept. But the practical significance of each cluster is determined by the users themselves, and the clustering algorithm will only be divided.The role of Clustering:1) can be used as a separate process for finding a distribution pattern of data2) as a preprocessing process for classification. First, classify data is clustered and then the