eval.py
def cvk(model, x, y, k=3):
  """
  Computes mean F1-score for a provided model and training data.
  
  Params:
    model - the model to use in cross-validation
    x - training features
    y - training labels
    k - number of cross-validation folds
    
  Returns:
    a numpy array of the mean F1-scores for each fold
  """
  return cvs(model, x, y, scoring=meanF1scorer, cv=k, n_jobs=3)

  Computes mean F1-score for a provided model and training data.
  
  Params:
    model - the model to use in cross-validation
    x - training features
    y - training labels
    k - number of cross-validation folds
    
  Returns:
    a numpy array of the mean F1-scores for each fold
  
Computes mean F1-score for a provided model and training data.

a numpy array of the mean F1-scores for each fold
Computes mean F1 - score for a provided model and training data . a numpy array of the mean F1 - scores for each fold
def cvk(model, x, y, k=3):
  return cvs(model, x, y, scoring=meanF1scorer, cv=k, n_jobs=3)
def cvk ( model , x , y , k = 3 ) : return cvs ( model , x , y , scoring = meanF1scorer , cv = k , n_jobs = 3 )
Computes mean F1-score for a provided model and training data.
Computes mean F1 - score for a provided model and training data .
"""
  Computes mean F1-score for a provided model and training data.
  
  Params:
    model - the model to use in cross-validation
    x - training features
    y - training labels
    k - number of cross-validation folds
    
  Returns:
    a numpy array of the mean F1-scores for each fold
  """
model
x
y
k