machine learning algorithms cheat sheet

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

Naive Bayesian algorithm is to look for a great posteriori hypothesis (MAP), which is the maximum posteriori probability of the candidate hypothesis.As follows:In Naive Bayes classifiers, it is assumed that the sample features are independent from one another:Calculate the posterior probability of each hypothesis and choose the maximum probability, and the corresponding category is the result of the sample classification.Advantages and DisadvantagesVery good for small-scale data, suitable for mu

In machine learning, there are many problems, there is no analytic form of solution, or analytic form of the solution but the computation is very large (for example, the problem of the least-squares solution), for such problems, we usually choose to use an iterative optimization method to solve.These commonly used optimization algorithms include gradient descent

attention to the fact that it is possible to encounter more than one classification probability in the actual operation or the probability of each classification is 0, at this time it is generally random to select a classification as the result. But sometimes it should be treated with care, such as using Bayesian to identify spam, if the probability is the same, even if the two probability difference is not large, it should be treated as non-spam, because the failure to identify the impact of s

each of the above three memory regions each time it performs GC, and most of the time it refers to the new generation. So the GC has two different types in the reclaimed area, one for the normal GC (minor GC) and one for the global GC (major GC or full GC), and they are for the following areas.Normal GC (minor GC): GC for Cenozoic regions only.Global GC (major GC or full GC): GC for all generational regions (Cenozoic, old generation, permanent generations).Because the GC effect is not good for

Evaluation algorithm Excellent program, commonly used a series of indicators to measure, mainly including: Precision,recall,f-1 score, why design these values? Can't I use precision alone?1, what is precision?Precison, accuracy, mainly indicates how much of the detected alert is the correct judgment (True POSITIVE,TP).In practice, due to the different proportions of normal and abnormal data in the sample, accuracy can not reflect the real algorithm performance, for example:Cancer detection: It i

used to calculate the conditional probabilities), and so on, based on the values of these studies to predict　　4, Naive Bayesian summaryThe advantages of Naive Bayes:1) Simple Bayesian model classification efficiency and stability2) The small-scale data set performance is very good, can deal with multi-classification problem, suitable for incremental training, especially when the data set out of memory, we can batch of training3) Less sensitive to missing data, simple algorithm, often used for t

does not change, but the function interval becomes twice times. So it's going to be normalized. Order | | w| | = 1; 2) Geometry interval So the above formula can tell the relationship between the function interval and the geometric interval:3. Maximum Interval The basic idea of support vector machine is: The super plane that can correctly divide the sample set and the maximum geometry interval. The optimization problem of the derivation constraint:

of finding the best fitting line is actually looking for the best b b and M M. In order to find the best fit line, here we first define what line is the best line. We define error (cost function): Error function errors (b,m) =1n∑1n ((B+MXI) −yi) 2 error functions \ error_{(b, M)}=\frac{1}{n}\sum_{1}^{n} ((b+mx_i)-y_i) ^{2} The Python code that calculates the loss function is as follows: # y = b + mx def compute_error_for_line_given_points (b, M, points): totalerror = SUM ((((b + M * point[