list of machine learning models

Inventory the difference between machine learning and statistical models Source: Public Number _datartisan data Craftsman (Shujugongjiang) In a variety of data science forums such a question is often asked-what is the difference between machine learning and sta

vectors:def cosineSimilarity(vec1: DoubleMatrix, vec2: DoubleMatrix): Double = { vec1.dot(vec2) / (vec1.norm2() * vec2.norm2()) }Now to check if it's right, pick a movie. See if it is 1 with its own similarity:val567val itemFactor = model.productFeatures.lookup(itemId).headvalnew DoubleMatrix(itemFactor)println(cosineSimilarity(itemVector, itemVector))Can see the result is 1!Next we calculate the similarity of other movies to it:valcase (id, factor) => valnew DoubleMatrix(factor)

) / (vec1.norm2() * vec2.norm2()) }Now to detect whether it is correct, choose a movie and see if it is 1 with its own similarity:val567val itemFactor = model.productFeatures.lookup(itemId).headvalnew DoubleMatrix(itemFactor)println(cosineSimilarity(itemVector, itemVector))You can see that the result is 1!Next we calculate the similarity of the other movies to it:valcase (id, factor) => valnew DoubleMatrix(factor) val sim = cosineSimilarity(factorVector, itemVector) (id,sim)

How to Evaluate machine learning Models, part 4:hyperparameter TuningIn the realm of machine learning, hyperparameter tuning is a "meta" learning task. It happens to is one of my favorite subjects because it can appear like black

[10] Knowing: The use of "regularization to prevent fit" in machine learning is a principle [11] multivariable linear regression Linear regression with multiple variable [of] CS229 lecture notes [Equivalence of regression and maximum entropy models [i] Linear SVM and LR have any similarities and differences. Under what conditions the SVM and logistic regression

Writing programming and writing machine learning modelsBased on the different machine learning models, a large number of characteristic variables are used to predict the fluctuation of the underlying asset price, and the prediction results are evaluated.

(that is, Xi in {1,..., | v|} Value in | V| is the vocabulary of the lexicon), n-word messages will be represented by a vector of length n, and the length of the vectors for different articles will probably not be the same.In the multiple event model, we assume that this is the case with the message: first determine whether this is a spam message through P (Y), and then independently determine each word by multiple distributions P (x|y). The probability of the final generation of the entire mes

Absolute Percent error average absolute percent errors), defined as follows:Compared with Rmse,mape, the error of each point is normalized, eliminating the effect of absolute error caused by individual outliers.Summary and extensionIn this article, we are based on three hypothetical Hulu scenarios, mainly explaining the importance of evaluating the choice of indicators. Each evaluation indicator has its value, but if the model is evaluated only from a single evaluation index, it often results i

(labels[:: -1]) Xgtrain = XGB. Dmatrix (Train[offset:,:], Label=labels[offset:]) Xgval = XGB. Dmatrix (Train[:offset,:], label=labels[:offset]) watchlist = [(Xgtrain, ' Train '), (Xgval, ' val ')]model = Xgb.train (plst , Xgtrain, Num_rounds, watchlist, early_stopping_rounds=120) preds2 = Model.predict (xgtest,ntree_limit=model.best_ Iteration) #combine Predictions#since the metric only cares on relative rank we don ' t need to Averagepreds = (PREDS1) * *. 4 + (PREDS2) *8.6return Preds(Code fro

: Known good data results are used for training| |Mathematical description of the problem--model training and performance evaluation--model deployment(2) Feature extraction and feature engineeringFeature extraction: (determines which features can be used to predict the target)The process of converting a free form of data, such as a word in a document, into a number in the form of rows and columnsFeature Engineering:Organize and combine features to achieve a richer information processAlgorithms t

the number of labels, and D is the sample dimension. In other words, each dimension is related to a feature.　　　　I=1~d, C=1~c　　　　That is, FJ corresponds to all the labels, and each label has a D f. is different. This can automatically generate all the required FJ (washing machine corresponding to 1~d number, the hair dryer will automatically correspond to the d+1~2d number ...) ), this is a naive FJ Setup method, which considers that some items in FJ

In this section, a linear model is introduced, and several linear models are compared, and the linear regression and the logistic regression are used for classification by the conversion error function.More important is this diagram, which explains why you can use linear regression or a logistic regression to replace linear classificationThen the stochastic gradient descent method is introduced, which is an improvement to the gradient descent method,

Discovery modeThe linear model and the neural network principle and the goal are basically consistent, the difference manifests in the derivation link. If you are familiar with the linear model, the neural network will be well understood, the model is actually a function from input to output, we want to use these models to find patterns in the data, to discover the existence of the function dependencies, of course, if the data itself exists such a fun

process statistics, analyze and visualize data. Through various examples, the reader can learn the core algorithm of machine learning, and can apply it to some strategic tasks, such as classification, prediction, recommendation. In addition, they can be used to implement some of the more advanced features, such as summarization and simplification. I've seen a part of this book before, but the internship in

techniques of algorithmic differentiation" This book is about automatic differentiation, and it seems that few people recommend it, but the quality of the content is pretty good. After reading it should be able to really know what is the BP algorithm, and why the deep learning framework to adopt the BP algorithm. It is recommended to implement the forward and posterior automatic differential algorithms in order to deepen understanding and memory.In a

This blog summarizes the individual in the learning process of some of the papers, code, materials and common resources and sites, in order to facilitate the recording of their own learning process, put it in the blog.Machine learning(1) Machine learning Video Library-caltec

:", end="") Print(sortedclasscount[0][0])returnSORTEDCLASSCOUNT[0][0]if __name__= ="__main__": start ()Output Result: Dataset.shape[0] Returns the number of rows in the matrix:4Dataset.shape[1] Returns the number of columns of a matrix:2(4, 2)dataset.shape Type:diffmat:[[2 1][1 0][2 2][ -1-2]]sqdiffmat:[[4 1][1 0][4 4][1 4]]sqdistances:[5 1 8 5]distance from unknown point to each known point: [2.23606798 1.2.82842712 2.23606798]index Position: [1 0 3 2]label 0:a1th visit, Clas

/bpr.htmlBibliographies on Neural Networkshttp://liinwww.ira.uka.de/bibliography/Neural/Intelligent Motion Control with an Artificial cerebellumHttp://www.q12.org/phd.htmlKernel Machineshttp://www.kernel-machines.org/Some Neural Networks Organizationshttp://www.ieee.org/nnc/http://www.inns.org/Neural Network Modeling in VisionHttp://www.rybak-et-al.net/nisms.htmlNeural Networks and machine learninghttp://learning.cs.toronto.edu/Neural application soft

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

form of a review. The book is divided into 10 chapters, which are related to sparse learning, implicit category analysis in crowdsourcing data, evolutionary optimization, deep learning, semi-supervised support vector machines, differential privacy protection, and machine learning applications in image quality evaluati