cross-validation

I am using recursive feature elimination with cross-validation (rfecv) with GridSearchCV with RandomForest classifier as follows using pipeline and without using pipeline . My code with pipeline is as follows. X = df[my_features_all] y = df['gold_standard'] #get development and testing sets x_train, x_test, y_train, y_test = train_test_split(X, y, random_state=0) from sklearn.pipeline import Pipeline #cross validation setting k_fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=0) #this is the classifier used for feature selection clf_featr_sele = RandomForestClassifier(random_state

I'm using python and I would like to use nested cross-validation with scikit learn. I have found a very good example : NUM_TRIALS = 30 non_nested_scores = np.zeros(NUM_TRIALS) nested_scores = np.zeros(NUM_TRIALS) # Choose cross-validation techniques for the inner and outer loops, # independently of the dataset. # E.g "LabelKFold", "LeaveOneOut", "LeaveOneLabelOut", etc. inner_cv = KFold(n_splits=4, shuffle=True, random_state=i) outer_cv = KFold(n_splits=4, shuffle=True, random_state=i) # Non_nested parameter search and scoring clf = GridSearchCV(estimator=svr, param_grid=p_grid, cv=inner_cv)

I want to run several regression types (Lasso, Ridge, ElasticNet and SVR) on a dataset with around 5,000 rows and 6 features. Linear regression. Use GridSearchCV for cross validation. The code is extensive but here are some critical parts: def splitTrainTestAdv(df): y = df.iloc[:,-5:] # last 5 columns X = df.iloc[:,:-5] # Except for last 5 columns #Scaling and Sampling X = StandardScaler().fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=0) return X_train, X_test, y_train, y_test def performSVR(x_train, y_train, X_test, parameter): C =

I am working in scikit and I am trying to tune my XGBoost. I made an attempt to use a nested cross-validation using the pipeline for the rescaling of the training folds (to avoid data leakage and overfitting) and in parallel with GridSearchCV for param tuning and cross_val_score to get the roc_auc score at the end. from imblearn.pipeline import Pipeline from sklearn.model_selection import RepeatedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import cross_val_score from xgboost import XGBClassifier std_scaling = StandardScaler() algo = XGBClassifier() steps

I am looking for an example of applying 10-fold cross-validation in neural network.I need something link answer of this question: Example of 10-fold SVM classification in MATLAB I would like to classify all 3 classes while in the example only two classes were considered. Edit: here is the code I wrote for iris example load fisheriris %# load iris dataset k=10; cvFolds = crossvalind('Kfold', species, k); %# get indices of 10-fold CV net = feedforwardnet(10); for i = 1:k %# for each fold testIdx = (cvFolds == i); %# get indices of test instances trainIdx = ~testIdx; %# get indices training