choosing machine learning classifier

. Classification model 1) training, testing. 2 Common methods: Naive Bayesian, maximum entropy, SVM. 6. Evaluation indicators 1) Accuracy rate Accuracy = (TP + TN)/(TP + FN + FP + TN) reflects the ability of the classifier to judge the whole sample--------------------positive judgment, negative judgment negative. 2) Accuracy rate Precision = tp/(TP+FP) reflects the proportion of the true positive sample in the positive case determined by the

Application Recommendations for machine learningFor a long time, the machine learning notes have not been updated, the last part of the updated neural network. This time we'll talk about the application of machine learning recommendations.Decide what to do nextSuppose we nee

solving process clearly. Readers with time can try step by step. I do not practice, because usually the task of the laboratory is busy, but some of the ideas can be borrowed from the work. (Reading is a lot of the time to know the same question how others do, but also divergent ideas). You can feel the way the author teaches us how to learn. Unlike many of the books that give the best solutions directly, the book begins with the most basic baseline, and then gradually discovers the problem

size as the input matrix.>>> Import knn>>> Reload (KNN) Six, the test algorithmone of the most important tasks in machine learning algorithms is to evaluate the correctness of the algorithm, usually we train the classifier with 90% of the existing data, and use the remaining 10% data to test the classifier to detect t

1. Decision Tree　　applicable conditions: The data of different class boundary is non-linear, and by continuously dividing the feature space into a matrix to simulate. There is a certain correlation between features. The number of feature values should be similar, because the information gain is biased towards more numerical characteristics.　　Advantages: 1. Intuitive decision-making rules; 2. Nonlinear characteristics can be handled; 3. The interaction between variables is considered.　　Disadvanta

artificially set before the model begins the learning process, rather than by training the parameter data (such as B, W) in the normal sense.These parameters define the concept of a higher level of the model (model complexity, learning capability, etc.).You cannot learn directly from the data in the Standard Model training process, you need to define it in advance.You can decide by setting different values

, there are n single classifiers, each single classifier has an equal error rate, and the single classifier is independent of each other, error rate is irrelevant. With these assumptions, we can calculate the error probability of the integration model:If n=11, the error rate is 0.25, to integrate the result prediction error, at least 6 single classifier predictio

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data 11.1 what to do firstIn the next video, I'll talk about the design of the machine learning system. These videos will talk about the major problems you will encounte

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

Internationally authoritative academic organization the IEEE International Conference on Data Mining (ICDM) selected ten classic algorithms for data Mining in December 2006: C4.5, K-means, SVM, Apriori, EM , PageRank, AdaBoost, KNN, Naive Bayes, and CART.Not only the top ten algorithms selected, in fact, participate in the selection of the 18 algorithms, in fact, casually come up with a kind of can be called Classic algorithm, they in the field of data mining has produced a very far-reaching imp

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

This paper is organized from the "machine learning combat" and Http://write.blog.csdn.net/posteditBasic Principles of Mathematics: Very simply, the Bayes formula: Base of thought: For an object to be sorted x, the probability that the thing belongs to each category Y1,y2, which is the most probability, think that the thing belongs to which category.Algorithm process: 1. Suppose something to be sorted x, it

Spark Machine Learning1 Online LearningThe model keeps updating itself as new messages are received, rather than being trained again and again, like offline training.2 Spark Streaming Discrete stream (DStream) Input source: Akka actors, Message queue, Flume, Kafka 、......Http://spark.apache.org/docs/latest/streaming-programming-guide.html Class group (Lineage): A collection of conversion operators and execution operators applied to th

1,C4.5C4.5 algorithm is a classification decision tree algorithm in machine learning algorithm, and its core algorithm is ID3 algorithm. The C4.5 algorithm inherits the advantages of the ID3 algorithm, and improves the ID3 algorithm in the following ways:1) Using the information gain rate to select the attribute, overcomes the disadvantage of choosing the attribu

Python machine learning Chinese version, python machine Chinese Version Introduction to Python Machine Learning Chapter 1 Let computers learn from data Convert data into knowledge Three types of machine

"Python Machine learning and practice – from scratch to the road to Kaggle race" very basicThe main introduction of Scikit-learn, incidentally introduced pandas, NumPy, Matplotlib, scipy.The code of this book is based on python2.x. But most can adapt to python3.5.x by modifying print ().The provided code uses Jupyter Notebook by default, and it is recommended to install ANACONDA3.The best is to https://www.

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data11.1 what to do firstThe next video will talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when desi

Machine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) BeginnerMachine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) Beginner[Email protected]Http://blog.csdn.net/zouxy09Machine lear