vikramsoni2

def plot_auc_pr(y_test, y_pred):
    
    fig = plt.figure(figsize=(20,7))

    plt.subplot(1,2,1)
    fpr, tpr, _ = roc_curve(y_test, y_pred)
    auc = roc_auc_score(y_test, y_pred)
    plt.plot(fpr,tpr,label="auc="+str(auc))
    plt.title('roc-auc curve')
    plt.legend(loc=4)

vikramsoni2

from bayes_opt import BayesianOptimization
from sklearn.cross_validation import KFold
import xgboost as xgb

def xgbCv(train, features, numRounds, eta, gamma, maxDepth, minChildWeight, subsample, colSample):   
   # prepare xgb parameters 
   params = {
            "objective": "reg:linear",
            "booster" : "gbtree",
            "eval_metric": "mae",