popular machine learning algorithms

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

Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- The main learning and research tasks of the last se

Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- After learning the implementation 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 Markov chainStep, set each word to a state, and then calculate the prob

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optimization of the hyper-parameters.　　The following algorithms are used primarily:Classification:· Random Forest· Gbm· Logistic Regression· Naive Bayes· Support Vector Machines· K-nearest NeighborsRegression:· Random Forest· Gbm· Linear Regression· Ridge· Lasso· SvrWhat parameters should I optimize? How can I select the most matching parameters? This is the two problems that people think about the most. It is not possible to answer this question wit

Regression, PLS), Sammon Mapping, multidimensional scale ( multi-dimensional scaling, MDS), projection tracking ( Projection Pursuit), and more. 1.3.12 Integration AlgorithmThe integrated algorithm trains the same sample independently with some relatively weak learning models, then integrates the results for overall prediction. The main difficulty of integration algorithm is how to integrate the independent weak

In the introduction of recommendation system, we give the general framework of recommendation system. Obviously, the recommendation method is the most core and key part of the whole recommendation system, which determines the performance of the recommended system to a large extent. At present, the main recommended methods include: Based on content recommendation, collaborative filtering recommendation, recommendation based on association rules, based on utility recommendation, based on knowledge

(Projection Pursuit), and more.1.3.12Integration AlgorithmsThe integrated algorithm trains the same sample independently with some relatively weak learning models, then integrates the results for overall prediction. The main difficulty of integration algorithm is how to integrate the independent weak learning models and how to integrate the learning results. Thi

parallel. However, partial parallelism can be achieved by self-sampling SGBT.8, GBDTAdvantages: 1, can flexibly deal with various types of data, including continuous and discrete values, processing classification and regression problems, 2, in the relatively few parameters of the time, the forecast preparation rate can also be relatively high. This is relative to the SVM, 3, can be used to filter features.4, using some robust loss function, the robustness of outliers is very strong. such as Hub

is all 0. And because it can be deduced that b=1nz∗zt=wt∗ (1NX∗XT) w=wt∗c∗w, this expression actually means that the function of the linear transformation matrix W in the PCA algorithm is to diagonalization the original covariance matrix C. Because diagonalization in linear algebra is obtained by solving eigenvalue and corresponding eigenvector, the process of PCA algorithm can be introduced (the process is mainly excerpted from Zhou Zhihua's "machine