machine learning techniques and algorithms

to Gini If the node cannot be divided, save the node as a leaf node Execute Two-dollar segmentation In the right subtree recursive call Createtree () method, create subtree In the right subtree recursive call Createtree () method, create subtree Four, cart and AdaBoost meta-algorithm application comparison Cart is more efficient than adaboost because the former is "conditionally cut" and the latter is completely "horizontal and vertical". Five, the characteristics of the

: 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

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

') 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

Supervised learningKNN k Nearest Neighbor algorithmdef classify0 (InX, DataSet, labels, k):Datasetsize = dataset.shape[0]Diffmat = Tile (InX, (datasetsize,1))-DataSetSqdiffmat = diffmat**2Sqdistances = Sqdiffmat.sum (Axis=1)distances = sqdistances**0.5classcount={}For I in range (k):Voteilabel = Labels[sorteddistindicies[i]]Classcount[voteilabel] = Classcount.get (voteilabel,0) + 1Sortedclasscount = sorted (Classcount.iteritems (), Key=operator.itemgetter (1), reverse=true)return sortedclasscoun

training samples.The above two or three can be done in the case of inverse existence, but what if the characteristics of the data are more than the sample points, because the inverse is not present at this time? You can use the ridge regression method to solve this problem, that is, it will be converted to, the other and the previous approach is similar.Of course, there is a method called forward stepwise regression, it is through each step to a certain weight increase or decrease a small value

paper is usually European-style distance, Pearson coefficient or cosine similarity.Assuming that a matrix A is established, the M*n matrix, the rows are all users, n is all items, each element of the matrix represents the user's rating of the item, then the item-based or user-based recommendation is to calculate the similarity of all columns or all rows. In real life, this matrix is very sparse.Topic: Recommend users to buy TOPN itemsThe Matrix C is a m*n matrix, each row represents each user,

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

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

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

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

, 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

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