mmc2015的专栏

下面给出一个拟合多项式的例子：[python] view plaincopyimport pandas as pd    import numpy as np  import scipy as sp  import matplotlib.pyplot as plt      from sklearn.pipeline

# Hyperparameter selection loopscore_hist = []Cvals = [0.001, 0.003, 0.006, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.1]for C in Cvals: model.C = C score = cv_loop(Xt, y, model, N) score_hi

print "Performing greedy feature selection..."score_hist = []N = 10good_features = set([])# Greedy feature selection loopwhile len(score_hist) score_hist[-2][0]: scores = [] for f in ran

拼接原始数据：train_data = pd.read_csv('train.csv')test_data = pd.read_csv('test.csv')all_data = np.vstack((train_data.ix[:,1:-1], test_data.ix[:,1:-1]))numpy下的合并数组vstack和hstack函数：>>> a = np.ones((

接上篇：def xgboost_pred(train,labels,test): params = {} params["objective"] = "reg:linear" params["eta"] = 0.005 params["min_child_weight"] = 6 params["subsample"] = 0.7 params["colsample

导入模块：import pandas as pdimport numpy as np #pandas依赖于numpyfrom sklearn import preprocessingimport xgboost as xgb常用功能简介：#load train and test train = pd.read_csv('train.csv', index_co

三张表；train_set.csv；test_set.csv；feature.csv。三张表通过object_id关联。import pandas as pdimport numpy as np# load training and test datasetstrain = pd.read_csv('../input/train_set.csv')test = pd.re