mohit-sinha

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

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

mohit-sinha

import pandas as pd;
import numpy as np;
import lightgbm as lgb
from bayes_opt import BayesianOptimization
from sklearn.model_selection import cross_val_score

def lgb_evaluate(                
                numLeaves,
                maxDepth,
                scaleWeight,