cross-validation

I am doing a regression using Generalized Linear Model.I am caught offguard using the crossVal function. My implementation so far; x = 'Some dataset, containing the input and the output' X = x(:,1:7); Y = x(:,8); cvpart = cvpartition(Y,'holdout',0.3); Xtrain = X(training(cvpart),:); Ytrain = Y(training(cvpart),:); Xtest = X(test(cvpart),:); Ytest = Y(test(cvpart),:); mdl = GeneralizedLinearModel.fit(Xtrain,Ytrain,'linear','distr','poisson'); Ypred = predict(mdl,Xtest); res = (Ypred - Ytest); RMSE_test = sqrt(mean(res.^2)); The code below is for calculating cross validation for mulitple

cv.glmnet has been used by most research papers and companies. While building a similar function like cv.glmnet for glmnet.cr (a similar package that implements the lasso for continuation ratio ordinal regression) I came across this problem in cv.glmnet . `cv.glmnet` first fits the model: glmnet.object = glmnet(x, y, weights = weights, offset = offset, lambda = lambda, ...) After the glmnet object is created with the complete data, the next step goes as follows: The lambda from the complete model fitted is extracted lambda = glmnet.object$lambda Now they make sure number of folds is more than

I am new to scikit, and have 2 slight issues to combine a data scale and grid search. Efficient scaler Considering a cross validation using Kfolds, I would like that each time we train the model on the K-1 folds, the data scaler (using preprocessing.StandardScaler() for instance) is fit only on the K-1 folds and then apply to the remaining fold. My impression is that the following code, will fit the scaler on the entire dataset, and therefore I would like to modify it to behave as described previsouly: classifier = svm.SVC(C=1) clf = make_pipeline(preprocessing.StandardScaler(), classifier)

I used hyperopt to search best parameters for SVM classifier, but Hyperopt says best 'kernel' is '0'. {'kernel': '0'} is obviously unsuitable. Does anyone know whether it's caused by my fault or a bag of hyperopt ? Code is below. from hyperopt import fmin, tpe, hp, rand import numpy as np from sklearn.metrics import accuracy_score from sklearn import svm from sklearn.cross_validation import StratifiedKFold parameter_space_svc = { 'C':hp.loguniform("C", np.log(1), np.log(100)), 'kernel':hp.choice('kernel',['rbf','poly']), 'gamma': hp.loguniform("gamma", np.log(0.001), np.log(0.1)), } from

I posted this question to Cross Validated forum and later realized may be this would find appropriate audience in stackoverlfow instead. I am looking for a way I can use the fit object (result) ontained from python statsmodel to feed into cross_val_score of scikit-learn cross_validation method? The attached link suggests that it may be possible but I have not succeeded. I am getting the following error estimator should a be an estimator implementing 'fit' method statsmodels.discrete.discrete_model.BinaryResultsWrapper object at 0x7fa6e801c590 was passed Refer this link Indeed, you cannot use