uni_multiStats.py
def runSKFold(n_seed, splits, data,target, columns_org, continuous, columns_dummified):
    '''
    Splitting the data into n_seed of splits folds cross validarion.
    
    Args:
        n_seed: number of cross-validation
        splits: number of folds in each cross validation
    
    Returns:
        data for each of n_seed * splits folds
    '''
    runs = []
    X = np.array(data.drop(target,axis=1))
    y = np.array(data[target])
    result = Parallel(n_jobs=-1)(delayed(execute_skfold)(X,y,columns_org, continuous, columns_dummified.drop(target), splits, seed) for seed in range(n_seed))
    for i in  result:
        for j in i:
            runs.append(j)
    return runs

    Splitting the data into n_seed of splits folds cross validarion.
    
    Args:
        n_seed: number of cross-validation
        splits: number of folds in each cross validation
    
    Returns:
        data for each of n_seed * splits folds
    
Splitting the data into n_seed of splits folds cross validarion.
Splitting the data into n_seed of splits folds cross validarion .
def runSKFold(n_seed, splits, data,target, columns_org, continuous, columns_dummified):
    runs = []
    X = np.array(data.drop(target,axis=1))
    y = np.array(data[target])
    result = Parallel(n_jobs=-1)(delayed(execute_skfold)(X,y,columns_org, continuous, columns_dummified.drop(target), splits, seed) for seed in range(n_seed))
    for i in  result:
        for j in i:
            runs.append(j)
    return runs
def runSKFold ( n_seed , splits , data , target , columns_org , continuous , columns_dummified ) : runs = [ ] X = np . array ( data . drop ( target , axis = 1 ) ) y = np . array ( data [ target ] ) result = Parallel ( n_jobs = - 1 ) ( delayed ( execute_skfold ) ( X , y , columns_org , continuous , columns_dummified . drop ( target ) , splits , seed ) for seed in range ( n_seed ) ) for i in result : for j in i : runs . append ( j ) return runs
Splitting the data into n_seed of splits folds cross validarion.
Splitting the data into n_seed of splits folds cross validarion .
'''
    Splitting the data into n_seed of splits folds cross validarion.
    
    Args:
        n_seed: number of cross-validation
        splits: number of folds in each cross validation
    
    Returns:
        data for each of n_seed * splits folds
    '''
data for each of n_seed * splits folds
data for each of n_seed * splits folds
n_seed
number of cross-validation
number of cross - validation
splits
number of folds in each cross validation
number of folds in each cross validation
data
target
columns_org
continuous
columns_dummified