types of machine learning models

Recommended BooksHere is a list of books which I had read and feel it was worth recommending to friends who was interested in computer Scie nCE.Machine Learningpattern recognition and machine learningChristopher M. BishopA new treatment of classic machine learning topics, such as classification, regression, and time series analysis from a Ba Yesian perspective. I

traditional safe operation has become the top priority of supplementing the scarce network security operation personnel. It is in this context that the use of a human-computer interactive machine learning engine can achieve the effect of automating data aggregation across different data types, collecting evaluation data to compliance requirements, normalizing in

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc

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

to build models and evaluate the model, the performance of the evaluation if it meets the requirements of the model to test other data, if not required to adjust the algorithm to re-establish the model, the evaluation again, so the cycle, and finally get the satisfaction of experience to deal with other data.1.2 Classification of machine learningMachine learning

process.2) Plug-in criteria are usually various types of error (0/1error,square error ... ), these errors are used to adjust the W weights and finally get the hypothesis (g) of the smaller error (in sample).3) This g is only good in the in sample, actually in the sample on the good performance and no eggs, because if you are only processing in the sample data, there is no need for machine

, so as to better identify the problem and adjust the model. The most noteworthy is the feature engineering , the characteristics of the design is often more like an art. In general or to accumulate more, more divergent thinking, hands-on to do, reflect on the summary, gradual.Review of each chapterGetting Started with 1.Python machine learning: This paper introduces the orientation of the book and

are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508 Training errors, cross-validation errors, and relationships between lambda graphs are represented as follows:When th

Customer Churn "Loss rate" is a business term that describes the customer's departure or stop payment of a product or service rate. This is a key figure in many organizations, as it is usually more expensive to get new customers than to retain the existing costs (in some cases, 5 to 20 times times the cost). Therefore, it is invaluable to understand that it is valuable to maintain customer engagement because it is a reasonable basis for developing retention policies and implementing operational

better (3)Machine learning Cornerstone Note 16--Machine How to learn better (4)XV, ValidationVerify.15.1 Model Selection problemModel selection issues.So far, many algorithmic models have been learned, but a model requires a lot of parameter selection, which is the focus of this chapter's discussion.Taking the two-yua

The Ames Razor principle (Occam ' s Razor)One sentence is said, "an explanation of the data should is mad as simple as possible,but no simpler".The meaning of machine learning is that the simplest explanation of the data is the best explanation (the simplest model, fits the data is also and the most plausible).For example, the picture above, the right is not better than the left to explain? That's obviously

-propagation. "Learning in pure Python is a pure Python machine learning Library. It can quickly build neural networks, conditional random-airports, logistic regression models, use INLINE-C optimization, easy to use and expand. ”Official homepage: http://montepython.sourceforge.net One. Theano Theano i

This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python machine learning time Guide. Learn the workflow of machine Learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'E:/python

determine whether the current model is underfitting or overfitting, the way to determine the following table:How to solve underfitting and overfitting problem?SummarizeIn summary, machine learning solves problems involving problem modeling, preparing training data, extracting features, training models, and optimizing models