machine learning algorithms cheat sheet

and is easily downloaded and modified by the reader.The following books will not be introduced, share the graphic coverHere is still to recommend my own built Python development Learning Group: 725479218, the group is the development of Python, if you are learning Python, small series welcome you to join, everyone is the software Development Party, not regularly share dry goods (only Python software develo

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

Recently learned about Python implementation of common machine learning algorithms on GitHubDirectory First, linear regression 1. Cost function2. Gradient Descent algorithm3. Normalization of the mean value4. Final running result5, using the linear model in the Scikit-learn library to implement Second, logistic regression 1. Cost funct

, activating the back of the nerve layer, the final output layer of the 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

machine Learning Algorithms Summary 1. Preface by using the machine learning algorithm to summarize the work, convenient for later search, rapid application. 2. Recommended Algorithms Cross Minimum Variance

conjugate gradient method is not only one of the most useful methods to solve the large scale linear equations,is also one of the most effective algorithms for solving large-scale nonlinear optimization. In various optimization algorithms, the conjugate gradient method is very important. Its advantage is that the required storage capacity is small, has step convergence, high stability, and does not require

the curve is above the Curve.The common convex functions are: exponential function f (x) =ax;a>1 Negative logarithm function? logax;a>1,x>0 Two-time function of opening up The decision of the convex function:1, If F is a first-order, x, y in any data domain satisfies F (y) ≥f (x) +f′ (x) (y?x)2. If f is a differentiable guide,Examples of convex optimization applications SVM: which consists of max|w| Turn min (12?| W|2) Least squares? The loss function of L

of the total number of features with non-0 weights)9. Logistic regression : Two-dollar category, extremely efficient Giallo Computer System (many problems need to use probability estimates as output) two ways: "As is" "converted to two-dollar category" Application: Automatic diagnosis of disease (to investigate the risk factors that cause disease, and to predict the probability of disease occurrence according to risk factors), economic forecasts and other fieldsCategory: Evaluation indicators:

Nine algorithms for machine learning---regressionTransferred from: http://blog.csdn.net/xiaohai1232/article/details/59551240Regression analysis is to quantify the size of the dependent variable affected by the independent variable, to establish a linear regression equation or a nonlinear regression equation, so as to predict the dependent variable, or the interpr

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on

, 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 probability of transitions between statesThis is the proba

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

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

KNN algorithm of ten Algorithms for machine learningThe previous period of time has been engaged in tkinter, machine learning wasted a while. Now want to re-write one, found a lot of problems, but eventually solved. We hope to make progress together with you.Gossip less, get to the point.KNN algorithm, also called near

classes more equal. but .....Recall, though,that better data often beats better algorithms, and designing good features goes a long. And if you had a huge dataset, your choice of classification algorithm might not really matter so much in terms of Classi Fication performance (so choose your algorithm based on speed or ease of use instead).And if you really-accuracy, you should definitely try a bunch of different classifiers and select the best one by

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

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