machine learning algorithms cheat sheet pdf

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

minimizing the degree of impurity at each step, the cart can handle the outliers and be able to handle the vacancy values. The termination condition of the tree partition: 1, the node achieves the complete purity; 2, the depth of the tree reaches the depth of the user3, the number of samples in the node belongs to the user specified number;Pruning method of tree is a pruning method of cost complexity;See details: http://blog.csdn.net/tianguokaka/article/details/9018933 Copyright NOTICE: This ar

, the message is the probability of classification C, when the word appears more time, will come to the problem of accuracy, you can dissolve the problem into a joint probability, that is, the probability of each word to find P (c| Wi), and then take out the probability of the largest topn to solve, such as n=10,n=15, and so on, the joint probability formula is as follows: p=p1*p2*p3*....pn/(p1*p2*p3*....pn+ (1-P1) * (1-P2) * (1-P3) ... * (1-PN)), where P1-PN is our chosen topn probability.

1. Linear modelSimple form, easy to model, good explanatory2. Logistic regressionNo prior assumptions about the data distribution;Approximate probability prediction can be obtained.Many numerical optimization algorithms can be directly used to calculate the optimal solution for the convex function of arbitrary order of the rate function.3. Linear discriminant Analysis (LDA)When two kinds of data are the same as prior, Gaussian distribution and covaria

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

(First chapter above)1.2.5 Linalg Linear Algebra LibraryBased on the basic operation of matrices, the Linalg Library of NumPy can satisfy most linear algebra operations.. determinant of matrices. Inverse of the Matrix. Symmetry of matrices. The rank of the matrix. The reversible matrix solves the linear equation1. Determinant of matrices from Import * in[#N-order matrix determinant operation in [6]: A = Mat ([[[1,2,3],[4,5,6],[7,8,9]]) in [print]det (A):"6.66133814775e-162. Inverse of the Matrix

Decision tree is to select the most information gain properties, classification.The core part is to use information gain to judge the classification performance of attributes. The information gain is calculated as follows:Information entropy:Multiple categories are allowed.Calculates the information gain for all attributes, choosing the largest root node as the decision tree. Then, the sample branches, continuing to determine the remaining properties of the information gain.Information gain has

value;If it becomes smaller, the new puzzle will replace the original;If it becomes larger, the probability of replacing the old one with the new one depends on the current temperature value, where the temperature will begin to slow down at a relatively high value, which is why the algorithm is more receptive to relatively poor performance in the early stages of execution, so that we can effectively avoid the possibility of falling into the local minimum, when the temperature reaches 0, The alg

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

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

value of 3.For example: Np.random.randint (3, 6, size=[2,3]) returns data with a dimension of 2x3. The value range is [3,6].(4). Random_integers (low[, high, size]), similar to the above randint, the difference between the range of values is closed interval [low, high].(5). Random_sample ([size]), returns the random floating-point number in the half-open interval [0.0, 1.0]. If it is another interval [a, b), it can be converted (b-a) * Random_sample ([size]) + AFor example: (5-2) *np.random.ran

ReferenceNB: High efficiency, easy to implement;LR: Less assumptions about data, strong adaptability, can be used for online learning, and the requirement of linearDecision tree: Easy to interpret, independent of data linearity or not; easy overfitting, no online supportRF: Fast and scalable, with few parameters, possibly over fittingSVM: High accuracy, processing of non-linear sub-data (high-dimensional data processing); Memory consumption, difficult

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

algorithm to initially estimate the number of K.2) How to choose the initial K pointsThe common algorithm is random selection. But often the effect is not very good, also can be similar to the method, the line uses the hierarchical clustering algorithm to divide the K clusters, and uses these clusters ' centroid as the initial centroid.3) method of calculating distancesCommonly used such as European distance, cosine angle similarity degree.4) Algorithm Stop conditionThe maximum number of iterat

other.Suppose we choose the attribute R as the split attribute, DataSet D, R has K different values {v1,v2,..., Vk}, so d according to the value of R into K-group {d1,d2,..., Dk}, after splitting by R, the amount of information required to separate the different classes of DataSet D is:information gain is defined as before and after the split, two of the amount is only poor:The following example uses Python to illustrate a decision tree construct using the information gain method:The main steps

application thread exists in the contents of the set logs, and modify the corresponding remembered sets, this step needs to pause the application, parallel running.Survival Object calculation and cleanup ( Live Data counting and Cleanup )It should be noted that in G1, it is not that final marking pause is executed, it is certain to perform cleanup this step, because this step needs to suspend the application, G1 in order to achieve quasi-real-time requirements, It is necessary to reasonably pla

Logistic regression is used to classify, and linear regression is used to return.Linear regression is the addition of the properties of the sample to the front plus the coefficients. The cost function is the sum of squared errors. Therefore, in the minimization of the cost function, you can directly derivative, so that the derivative equals 0, as follows:Gradient descent can also be used to learn the same gradient as the logistic regression form.Advantages of linear regression: simple calculatio