types 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)

[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

results are garbage. It can be seen that the key to the success of the model depends not only on the selection of the model, but also on whether we have found a valid input based on a particular problem. The commonly used data can be divided into structured data and unstructured data, in which: structured data can be regarded as a table of relational database, each column has a clear definition, contains two basic types of numerical, category, and un

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

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.

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

(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

: 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,

(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

Types of learning according to my personal understanding, the classification of learning methods in machine learning helps us face a specific problem, you can select an appropriate machine lea