问不适当，过拟合，Good_Generalization

EN

from sklearn.svm import SVC
from sklearn.model_selection import validation_curve

def answer_six():
    # SVC requires kernel='rbf', C=1, random_state=0 as instructed
    # C: Penalty parameter C of the error term
    # random_state: The seed of the pseudo random number generator 
    # used when shuffling the data for probability estimates
    # e radial basis function kernel, or RBF kernel, is a popular 
    # kernel function used in various kernelized learning algorithms, 
    # In particular, it is commonly used in support vector machine 
    # classification

    model = SVC(kernel='rbf', C=1, random_state=0)

    # Return numpy array numbers spaced evenly on a log scale (start, 
    # stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)

    gamma = np.logspace(-4,1,6)

    # Create a Validation Curve for model and subsets.
    # Create parameter name and range regarding gamma. Test Scoring 
    # requires accuracy. 
    # Validation curve requires X and y.

    train_scores, test_scores = validation_curve(model, X_subset, y_subset, param_name='gamma', param_range=gamma, scoring ='accuracy')

    # Determine mean for scores and tests along columns (axis=1)
    sc = (train_scores.mean(axis=1), test_scores.mean(axis=1))                                                 

    return sc

answer_six()