machine learning algorithms cheat sheet

Machine learning algorithms are numerous, and various algorithms involve more parameters, this article will briefly introduce the RF,GBDT and other algorithms of tuning experience and steps. 1. BP Tuning matters1.BP is sensitive to feature scaling, first scale data.2. Experi

(i) Understanding decision Trees1, decision tree Classification principleRecent surveys have shown that decision trees are also the most frequently used data mining algorithms, and the concept is simple. One of the most important reasons why a decision tree algorithm is so popular is that the user does not have to understand the machine learning algorithm, nor do

In opencv3.0, a ml.cpp file is provided, all of which are machine learning algorithms, providing a total of a few:1. Normal Bayesian: Normal Bayessian classifier I have introduced in another article blog post: Realization of machine learning in Opencv3: using normal Bayesian

specific job requirements, image algorithm For example, now deep learning hot not I said, so the basic convolution neural network algorithm , image classification , image detection The more famous paper in recent years should read it. If you have a condition, use it like a caffe,tensorflow frame.2. Machine Learning EngineerThis post is basically the same as the

; Rsold =r " *R; for i=1:length (b) Ap =a*P; Alpha =rsold/(p " *ap); X=x+alpha*P; R =r-alpha*AP; Rsnew =r " *R; if sqrt (rsnew) break ; End P =r+ (rsnew/rsold) *P; Rsold =rsnew; EndEnd Back to top of 4. Heuristic Optimization methodHeuristic method refers to the method that people take when they solve the problem and find it according to the rule of experience. It is characterized by the use of past experience in the solution of problems, th

equal to the distance between the other two. This red line is the hyperplane that SVM is looking for in two-dimensional situations. It is used for binary classification data. The point supporting the other two online is the so-called support vector. We can see that there is no sample in the middle of the hyperplane and the other two lines. After finding this hyperplane, we use the mathematical representation of the hyperplane data to perform binary classification of the sample data, which is th

In machine learning, are more data always better than better algorithms? No. There is times when more data helps, there is times when it doesn ' t. Probably One of the most famous quotes Defen Ding the power of data is that of Google ' s Directorpeter norvigclaiming that" We Don has better algorithms. We just has mor

Machine Learning Algorithms Summary: Linear regression (Linear Regression) (ml category) y=ax+b Use continuity variables to estimate actual values The optimal linear relationship between the independent variable and the dependent variable is identified by the linear regression algorithm, and an optimal line can be determined on the

Learning machine learning algorithms is really a headache, we have so many papers, books, websites can be consulted, they are either refined mathematical description (mathematically), or a step-by-Step text Introduction (textually). If you're lucky enough, you might find some pseudo-code. If the character breaks out, y

Machines (SVM), referred to as the SV Machine (the general abbreviation in the paper). It is a supervised learning method, which is widely used in statistical classification and regression analysis. Support Vector machines map vectors to a higher dimensional space, where a maximum interval of hyperspace is established in this space. On both sides of the super plane that separates the data, there are two su

1 Scenario Resolution: A. Data exploration (size of data, missing or garbled data, ETL operation, field type, whether or not the target queue is included)B. Scene abstraction (it is through the existing data, to dig out the business scenarios can be applied.) Machine learning is primarily used to address scenarios including two classification, multi-classification, clustering, and regression.C. Algorithm se

This week school things more so dragged a few days, this time we talk about clustering algorithm ha.First of all, we know that the main machine learning methods are divided into supervised learning and unsupervised learning. Supervised learning mainly refers to we have given

SAS graphical user interfaces help you build machine-learning models and implement an iterative machine learning process. You don ' t have a advanced statistician. Our comprehensive selection of the machine learning

Nine algorithms for machine learning---naive Bayesian classifierTo understand the Naive Bayes classificationBayesian classification is a generic term for a class of classification algorithms, which are based on Bayesian theorem, so collectively referred to as Bayesian classification. Naive naive Bayesian classification

hope for in the earthquake prediction is that the recall is very high, that is to say, every earthquake we want to predict. We can sacrifice precision at this time. 1000 alarms are preferred, 10 earthquakes are predicted correctly, and do not predict 100 times 8 leaks two times. Suspects convictedBased on the principle of not blaming a good man, we hope to be very accurate about the conviction of a suspect. In time, some criminals were spared (recall low), but also worthwhile. Regressi

of experience. It is characterized by the use of past experience in the solution of problems, the selection of methods that have been effective, rather than the systematic and determined steps to seek answers. There are many kinds of heuristic optimization methods, including classical simulated annealing method, genetic algorithm, ant colony algorithm, particle swarm algorithm and so on.There is also a special optimization algorithm called multi-Objective optimization algorithm, which is mainly

A simple introduction to machine learning algorithms.As the team (Big Data Team) technology development needs, through the traffic business data needs to expand, to achieve data mining and data analysis technology mastery, bypassing the machine learning algorithm, it can be said that the core value of big data lies in

the depth of decision tree(2) The structure of the tree changes due to a little change in the sample, which can be improved by integrated learning.Application:(1) Financial options for option pricing are of great use(2) Remote sensing is the application field of pattern recognition based on decision Tree(3) Banks use decision tree algorithm to classify the probability of default payment by loan applicant(4)Gerber Products Inc., a popular baby products company, uses decision tree

') 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

For the following three reasons, we chose python as the programming language for implementing machine learning algorithms: (1) Clear Python syntax; (2) Easy to operate plain text files; (3) widely used, there are a large number of development documents. Executable pseudocode Python has a clear syntax structure and is also called executable pseudo-code ). The defa