top machine learning algorithms

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

Continuous update ...1.k-Nearest Neighbor algorithmAdvantages: High precision, insensitive to outliers, no data input settingsCons: High computational complexity, high spatial complexityApplicable data range: Numerical and nominal typeApplicable scenarios:2.ID3 Decision Tree AlgorithmAdvantages: The computational complexity is not high, the output is easy to understand, the missing middle value is not sensitive, can process the irrelevant characteristic dataDisadvantage: May cause over-matching

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

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[

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

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:

intention. Look at the judging criteria below. Using p to express precision,r expression recall; If we choose the criterion = (p+r)/2, then algorithm3 win, obviously unreasonable. Here we introduce an evaluation standard: F1-score. When p = or r=0, there is f=0; When P=1r=1, there is f=1, the largest; Similarly, we apply F1 score to the above three algorithms, and the results are ALGORITHM1 largest, which is the best; algorithm3 the least, the worst

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning) PDF Video Keras Example application-handwriting Digit recognition Step 1

analyzes the theoretical basis of evolutionary optimization for most evolutionary algorithms, which often depend on the insufficiency of heuristic algorithms. By drawing on the multi-layered framework of deep learning, Professor Chen Yu has developed hierarchical Bayesian analysis and online variable decibel Dean inference method in the 4th chapter. In the 5th c

model and re-experiment to optimize them. (ii) Criteria for numerical evaluation of machine learning algorithms 1. Cross-validation set error (accuracy) This is a good idea, the design of the fitting function if the cross-validation set test error is very large, then certainly not a good learning algorithm; However,

Objective:When looking for a job (IT industry), in addition to the common software development, machine learning positions can also be regarded as a choice, many computer graduate students will contact this, if your research direction is machine learning/data mining and so on, and it is very interested in, you can cons

learning to organize the daily learning of machine learning algorithms, and practical problems, do more experiments, and strive to get a better learning effect, I will be firm belief, more efforts to catch up with the pace of exc

Objective Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on. Here, the main understanding of supervision and unsu

Classification and logistic regression (classification and logistic regression)Http://www.cnblogs.com/czdbest/p/5768467.htmlGeneralized linear model (generalized Linear Models)Http://www.cnblogs.com/czdbest/p/5769326.htmlGenerate Learning Algorithm (generative learning algorithms)Http://www.cnblogs.com/czdbest/p/5771500.htmlClassification and logistic regression

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

Dr. Hangyuan Li's "Talking about my understanding of machine learning" machine learning and natural language processing [Date: 2015-01-14] Source: Sina Weibo Hangyuan Li [Font: Big Small] Calculating time, from the beginning to the present, do m