deploy machine learning model flask

returnWhen the classification problem is no longer two yuan but K yuan, that is, y∈{1,2,..., k}. We can solve this classification problem by constructing the generalized linear model. The following steps are described.Suppose y obeys exponential family distribution, φi = P (y = i;φ) and known. So. We also define.Also 1{} The condition for the representation in parentheses is the true value of the entire equation is 1, otherwise 0. So (T (y)) i = 1{y

distribution, in accordance with the joint distribution of the query, we can obtain pi.Q's design is said to be a value of 60W knife annual salary job, dare not to speculate. Here we assume that Q is given (UNIFORM/SW) **********************************************The MH sampling process is as follows:1, given assignment, according to the F to find Pi (Assignment)2, according to the above formula to calculate the acceptance probability a3, decide whether to accept, complete the sampling update

different assumptions, we have different functions, such as maps from X to Y. This is how we mathematically define neural network assumptions.4. Model Representation II 5. Examples and intuitions IThe problem of classification of "and", "or" is solved by using neural network. 6. Examples and intuitions II Neural networks can also be used to identify handwritten numbers. The input it uses is a different image or just some original pixel points. The

logistic regression is a two classification problem, obeys the Bernoulli distribution, the output result is expressed in the form of probability, can write the expression　　To facilitate the subsequent analysis, we integrate the segmented function　　For a given training sample, this is what has happened, in the probability of statistics that has happened should be the most probability of the event (the probability of a small event is not easy to happen), so you can use the maximum likelihood meth

If you choose a model from many models for a learning problem, you can balance deviations and variances, how can you choose automatically? For example, using the polynomial regression model h (x) =g (Θ0+Θ1X+Θ2X2+...+ΘKXK), you want to automatically determine the value of K and choose between 0~10. For example, to automatically select the bandwidth parameter in lo

basic type, the virtual machine immediately creates a new array class for that element type, and the dimension is determined at this time. Then, an instance of the class is created to represent this type. For referenced arrays, arrays are marked as defined by the classloader that defines their element types. For an array of basic types, the array class is marked as defined by the startup class loader. Analysis of Non-array classes and interfaces: Sin

node.Right-click the node, tap Excute, then right-click the decision Tree model to view the results.9 test the model with a test data set and spark Predictor node.Copy the CSV reader,missing value and table to spark node and refer to 3,4,6 step to configure the read test data set and process and convert the data. Add the Spark Predictor node, configure the Spark Predictor node, and connect the newly added

training set is to think of ways to get the most out of a set of parameters. It is advisable to take the logarithm of probability to make the equation linearized.OK, so far, the expression of the derivative of each parameter has been calculated, F_j (x, y) is very easy to find, for any training set this is known. And the later E is more troublesome, it needs to bring all the feasible tags into the FJ, and Times p (here p also, given wj,p is known) but this kind of gradient calculation is very l

than GLM. To achieve optimal results, you should try different cost super-parametersRadial.svm.fit Predictions Predictions MSE Mse#错误率, 0.1421538, is higher than just now, so it is known that the radial kernel function is not effective, so the boundary may be linear. So the GLM effect will be better.#下面试一下KNN, KNN is good for nonlinear effectLibrary (' class ')Knn.fit Predictions MSE Mse#错误率0.1396923, it is true that it is possible to have a linear model