I've been reading the articles about mixed logit model, but I wonder whether mixed logit is one of the examples of nonparametric model.
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I want to optimally find the clusters and assignment of each subject into the correct cohort in the nonlinear mixed effect framework. I came to know a package in R which is lcmm that calls this type of modeling a latent class mixture model. They have the clustering of the linear mixed effect model in hlme function. I am wondering if there is a package that deals with the latent class/clustering of the nonlinear mixed effect modeling? Any help is appreciated.
I have two datasets and I want to train a SVM classification model (fitcsvm) by one of them and then predict labels for the other one. I use 10-fold cross-validation (crossval) to train my model so I have 10 different models. My question is which one of these models are the best for prediction and how can I find that?
here is my code:
Mdl = fitcsvm(trainingData,labels);
CVMdl = crossval(Mdl);
You may have mixed up something here. The function fitcsvm trains a single model and the function crossval validates this single model. It will then return an evaluation value.
In general, you cannot train a model by cross-validation (as it says, it is a validation technique). However, you can use cross-validation to train good models.
What you are looking for is a sort of hyperparameter optimization. Those are methods that automatically train multiple models on a given data set to find the best tuning values for the SVM. Have a look at the docs here
You can turn it on like this
Mdl = fitcsvm(trainingData,labels,'OptimizeHyperparameters','auto')
You may want to use cross-validation to train multiple models with the same tuning parameters but I guess, you'll have to write this yourself. Perhaps this already helps you.
Hello data science community,
I want to build a recommender system based on classification models,Can I use a classification model such as svm or decision trees to predict the label so i can recommend the contents or products based on the prediction?
Cross-validation is used for estimation of model.
I misunderstand the concept. If different part of corpus is used for training per each iteration, then each iteration generates the different model. So, what's validated? Only selected features?
I need simple example about how to use keras model. It is not clear for me what difference between model.evaluate and model.predict.
I want to create model for binary classification. Lets say I have images of cats and dogs, train model and can use it to predict which animal on given photo. Maybe there is some good into or tutorials. I read anything in first five pages in google, but found only complex level tutorials and discussions.
To make things short:
model.evaluate evaluates a pair (X,Y) and returns the loss (and all other metrics configured for the model). This is for testing your model on a vaildation or test set.
model.predict predicts the outcome given an input X. This if for predicting the class from an input image for example.
This, among other things, is also clearly documented in the linked documentation.
You can find a lot of example models for Keras in the git repository (keras/examples) or on the Keras website (here and here).
For binary classification you could use this model for example:
model = Sequential()
model.add(Dense(300, init='uniform'))
model.add(Activation('relu'))
model.add(Dense(2, init='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.02))
model.fit(X, Y)