machine learning sample code

boosting analysis, but it still cannot be explained that when the training error is 0, its generalization error is still decreasing, later scholars have raised the question of margin bound. In addition, the method of better understanding of boosing from another perspective is greedy boosting, that is, the process of searching for sample weight D and weak classifier weight W is a greedy process. Finally, the teacher talked about a general loss functio

TensorFlow integrates and implements a variety of machine learning-based algorithms that can be called directly.Supervised learning1) Decision Trees (decision tree)Decision tree is a tree structure, providing people with decision-making basis, decision tree can be used to answer yes and no problem, it through the tree structure of the various situations are represented, each branch represents a choice (sele

few years there have been many algorithms for machine learning, including decision tree learning, inductive logic programming, clustering Analysis (clustering), reinforcement learning, Bayesian networks, etc. As we all know, no one really achieve the ultimate goal of "strong artificial intelligence", using the early

public. Of course, there is a good advantage to compressing large values into this range, which is to eliminate the effects of particularly conspicuous variables (not knowing if they are correct). The realization of this great function in fact only needs a trivial one, that is, in the output plus a logistic function. In addition, for the two classification, it is simple to think: if the probability of the sample x belongs to a positive class is great

　　Originally this article is prepared for 5.15 more, but the last week has been busy visa and work, no time to postpone, now finally have time to write learning Spark last part of the content.第10-11 is mainly about spark streaming and Mllib. We know that Spark is doing a good job of working with data offline, so how does it behave on real-time data? In actual production, we often need to deal with the received data, such as real-time

years there have been many algorithmic approaches, including decision tree learning, inductive logic programming, cluster analysis (clustering), reinforcement learning, Bayesian networks, and so on. As we all know, no one really achieves the ultimate goal of "strong AI", using the early machine learning method, we eve

generalization error;Easy to explain;Low computational complexity;Disadvantages:It is sensitive to the selection of parameters and kernel functions;The original SVM is only better at dealing with two classification problems;Boosting:Mainly take AdaBoost as an example, first look at the flow chart of AdaBoost, as follows:As you can see, we need to train several weak classifiers during training (3 in the figure), each weak classifier is trained by a sample