摘要：字符串函數(shù)名，或是可調(diào)用對象，需要其函數(shù)簽名形如如果是，則使用的誤差估計(jì)函數(shù)。運(yùn)行后的結(jié)果為每輪迭代運(yùn)行結(jié)果參數(shù)的最佳取值最佳模型得分由輸出結(jié)果可知參數(shù)的最佳取值。提醒一點(diǎn)，這個(gè)分?jǐn)?shù)是根據(jù)前面設(shè)置的得分函數(shù)算出來的，即中的。

這一篇博客的內(nèi)容是在上一篇博客Scikit中的特征選擇，XGboost進(jìn)行回歸預(yù)測，模型優(yōu)化的實(shí)戰(zhàn)的基礎(chǔ)上進(jìn)行調(diào)參優(yōu)化的，所以在閱讀本篇博客之前，請先移步看一下上一篇文章。

我前面所做的工作基本都是關(guān)于特征選擇的，這里我想寫的是關(guān)于XGBoost參數(shù)調(diào)整的一些小經(jīng)驗(yàn)。之前我在網(wǎng)站上也看到很多相關(guān)的內(nèi)容，基本是翻譯自一篇英文的博客，更坑的是很多文章步驟講的不完整，新人看了很容易一頭霧水。由于本人也是一個(gè)新手，在這過程中也踩了很多大坑，希望這篇博客能夠幫助到大家！下面，就進(jìn)入正題吧。

首先，很幸運(yùn)的是，Scikit-learn中提供了一個(gè)函數(shù)可以幫助我們更好地進(jìn)行調(diào)參：

sklearn.model_selection.GridSearchCV

常用參數(shù)解讀：

estimator：所使用的分類器，如果比賽中使用的是XGBoost的話，就是生成的model。比如： model = xgb.XGBRegressor(**other_params)

param_grid：值為字典或者列表，即需要最優(yōu)化的參數(shù)的取值。比如：cv_params = {"n_estimators": [550, 575, 600, 650, 675]}

scoring :準(zhǔn)確度評價(jià)標(biāo)準(zhǔn)，默認(rèn)None,這時(shí)需要使用score函數(shù)；或者如scoring="roc_auc"，根據(jù)所選模型不同，評價(jià)準(zhǔn)則不同。字符串（函數(shù)名），或是可調(diào)用對象，需要其函數(shù)簽名形如：scorer(estimator, X, y)；如果是None，則使用estimator的誤差估計(jì)函數(shù)。scoring參數(shù)選擇如下：

具體參考地址：http://scikit-learn.org/stable/modules/model_evaluation.html

這次實(shí)戰(zhàn)我使用的是r2這個(gè)得分函數(shù)，當(dāng)然大家也可以根據(jù)自己的實(shí)際需要來選擇。

調(diào)參剛開始的時(shí)候，一般要先初始化一些值：

learning_rate: 0.1
n_estimators: 500
max_depth: 5
min_child_weight: 1
subsample: 0.8
colsample_bytree:0.8
gamma: 0
reg_alpha: 0
reg_lambda: 1

鏈接：XGBoost常用參數(shù)一覽表

你可以按照自己的實(shí)際情況來設(shè)置初始值，上面的也只是一些經(jīng)驗(yàn)之談吧。

調(diào)參的時(shí)候一般按照以下順序來進(jìn)行：

1、最佳迭代次數(shù)：n_estimators

if __name__ == "__main__":
    trainFilePath = "dataset/soccer/train.csv"
    testFilePath = "dataset/soccer/test.csv"
    data = pd.read_csv(trainFilePath)
    X_train, y_train = featureSet(data)
    X_test = loadTestData(testFilePath)

    cv_params = {"n_estimators": [400, 500, 600, 700, 800]}
    other_params = {"learning_rate": 0.1, "n_estimators": 500, "max_depth": 5, "min_child_weight": 1, "seed": 0,
                    "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0, "reg_alpha": 0, "reg_lambda": 1}

    model = xgb.XGBRegressor(**other_params)
    optimized_GBM = GridSearchCV(estimator=model, param_grid=cv_params, scoring="r2", cv=5, verbose=1, n_jobs=4)
    optimized_GBM.fit(X_train, y_train)
    evalute_result = optimized_GBM.grid_scores_
    print("每輪迭代運(yùn)行結(jié)果:{0}".format(evalute_result))
    print("參數(shù)的最佳取值：{0}".format(optimized_GBM.best_params_))
    print("最佳模型得分:{0}".format(optimized_GBM.best_score_))

寫到這里，需要提醒大家，在代碼中有一處很關(guān)鍵：

model = xgb.XGBRegressor(**other_params)中兩個(gè)*號千萬不能省略！可能很多人不注意，再加上網(wǎng)上很多教程估計(jì)是從別人那里直接拷貝，沒有運(yùn)行結(jié)果，所以直接就用了 model = xgb.XGBRegressor(other_params)。悲劇的是，如果直接這樣運(yùn)行的話，會報(bào)如下錯(cuò)誤：

xgboost.core.XGBoostError: b"Invalid Parameter format for max_depth expect int but value...

不信，請看鏈接：xgboost issue

以上是血的教訓(xùn)啊，自己不運(yùn)行一遍代碼，永遠(yuǎn)不知道會出現(xiàn)什么Bug！

運(yùn)行后的結(jié)果為：

[Parallel(n_jobs=4)]: Done  25 out of  25 | elapsed:  1.5min finished
每輪迭代運(yùn)行結(jié)果:[mean: 0.94051, std: 0.01244, params: {"n_estimators": 400}, mean: 0.94057, std: 0.01244, params: {"n_estimators": 500}, mean: 0.94061, std: 0.01230, params: {"n_estimators": 600}, mean: 0.94060, std: 0.01223, params: {"n_estimators": 700}, mean: 0.94058, std: 0.01231, params: {"n_estimators": 800}]
參數(shù)的最佳取值：{"n_estimators": 600}
最佳模型得分:0.9406056804545407

由輸出結(jié)果可知最佳迭代次數(shù)為600次。但是，我們還不能認(rèn)為這是最終的結(jié)果，由于設(shè)置的間隔太大，所以，我又測試了一組參數(shù)，這次粒度小一些：

 cv_params = {"n_estimators": [550, 575, 600, 650, 675]}
    other_params = {"learning_rate": 0.1, "n_estimators": 600, "max_depth": 5, "min_child_weight": 1, "seed": 0,
                    "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0, "reg_alpha": 0, "reg_lambda": 1}

運(yùn)行后的結(jié)果為：

[Parallel(n_jobs=4)]: Done  25 out of  25 | elapsed:  1.5min finished
每輪迭代運(yùn)行結(jié)果:[mean: 0.94065, std: 0.01237, params: {"n_estimators": 550}, mean: 0.94064, std: 0.01234, params: {"n_estimators": 575}, mean: 0.94061, std: 0.01230, params: {"n_estimators": 600}, mean: 0.94060, std: 0.01226, params: {"n_estimators": 650}, mean: 0.94060, std: 0.01224, params: {"n_estimators": 675}]
參數(shù)的最佳取值：{"n_estimators": 550}
最佳模型得分:0.9406545392685364

果不其然，最佳迭代次數(shù)變成了550。有人可能會問，那還要不要繼續(xù)縮小粒度測試下去呢？這個(gè)我覺得可以看個(gè)人情況，如果你想要更高的精度，當(dāng)然是粒度越小，結(jié)果越準(zhǔn)確，大家可以自己慢慢去調(diào)試，我在這里就不一一去做了。

2、接下來要調(diào)試的參數(shù)是min_child_weight以及max_depth：

注意：每次調(diào)完一個(gè)參數(shù)，要把 other_params對應(yīng)的參數(shù)更新為最優(yōu)值。

 cv_params = {"max_depth": [3, 4, 5, 6, 7, 8, 9, 10], "min_child_weight": [1, 2, 3, 4, 5, 6]}
    other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 5, "min_child_weight": 1, "seed": 0,
                    "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0, "reg_alpha": 0, "reg_lambda": 1}

運(yùn)行后的結(jié)果為：

[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:  1.7min
[Parallel(n_jobs=4)]: Done 192 tasks      | elapsed: 12.3min
[Parallel(n_jobs=4)]: Done 240 out of 240 | elapsed: 17.2min finished
每輪迭代運(yùn)行結(jié)果:[mean: 0.93967, std: 0.01334, params: {"min_child_weight": 1, "max_depth": 3}, mean: 0.93826, std: 0.01202, params: {"min_child_weight": 2, "max_depth": 3}, mean: 0.93739, std: 0.01265, params: {"min_child_weight": 3, "max_depth": 3}, mean: 0.93827, std: 0.01285, params: {"min_child_weight": 4, "max_depth": 3}, mean: 0.93680, std: 0.01219, params: {"min_child_weight": 5, "max_depth": 3}, mean: 0.93640, std: 0.01231, params: {"min_child_weight": 6, "max_depth": 3}, mean: 0.94277, std: 0.01395, params: {"min_child_weight": 1, "max_depth": 4}, mean: 0.94261, std: 0.01173, params: {"min_child_weight": 2, "max_depth": 4}, mean: 0.94276, std: 0.01329...]
參數(shù)的最佳取值：{"min_child_weight": 5, "max_depth": 4}
最佳模型得分:0.94369522247392

由輸出結(jié)果可知參數(shù)的最佳取值：{"min_child_weight": 5, "max_depth": 4}。（代碼輸出結(jié)果被我省略了一部分，因?yàn)榻Y(jié)果太長了，以下也是如此）

3、接著我們就開始調(diào)試參數(shù)：gamma：

cv_params = {"gamma": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]}
    other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 4, "min_child_weight": 5, "seed": 0,
                    "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0, "reg_alpha": 0, "reg_lambda": 1}

運(yùn)行后的結(jié)果為：

[Parallel(n_jobs=4)]: Done  30 out of  30 | elapsed:  1.5min finished
每輪迭代運(yùn)行結(jié)果:[mean: 0.94370, std: 0.01010, params: {"gamma": 0.1}, mean: 0.94370, std: 0.01010, params: {"gamma": 0.2}, mean: 0.94370, std: 0.01010, params: {"gamma": 0.3}, mean: 0.94370, std: 0.01010, params: {"gamma": 0.4}, mean: 0.94370, std: 0.01010, params: {"gamma": 0.5}, mean: 0.94370, std: 0.01010, params: {"gamma": 0.6}]
參數(shù)的最佳取值：{"gamma": 0.1}
最佳模型得分:0.94369522247392

由輸出結(jié)果可知參數(shù)的最佳取值：{"gamma": 0.1}。

4、接著是subsample以及colsample_bytree：

cv_params = {"subsample": [0.6, 0.7, 0.8, 0.9], "colsample_bytree": [0.6, 0.7, 0.8, 0.9]}
    other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 4, "min_child_weight": 5, "seed": 0,
                    "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0.1, "reg_alpha": 0, "reg_lambda": 1}

運(yùn)行后的結(jié)果顯示參數(shù)的最佳取值：{"subsample": 0.7,"colsample_bytree": 0.7}

5、緊接著就是：reg_alpha以及reg_lambda：

 cv_params = {"reg_alpha": [0.05, 0.1, 1, 2, 3], "reg_lambda": [0.05, 0.1, 1, 2, 3]}
    other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 4, "min_child_weight": 5, "seed": 0,
                    "subsample": 0.7, "colsample_bytree": 0.7, "gamma": 0.1, "reg_alpha": 0, "reg_lambda": 1}

運(yùn)行后的結(jié)果為：

[Parallel(n_jobs=4)]: Done  42 tasks      | elapsed:  2.0min
[Parallel(n_jobs=4)]: Done 125 out of 125 | elapsed:  5.6min finished
每輪迭代運(yùn)行結(jié)果:[mean: 0.94169, std: 0.00997, params: {"reg_alpha": 0.01, "reg_lambda": 0.01}, mean: 0.94112, std: 0.01086, params: {"reg_alpha": 0.01, "reg_lambda": 0.05}, mean: 0.94153, std: 0.01093, params: {"reg_alpha": 0.01, "reg_lambda": 0.1}, mean: 0.94400, std: 0.01090, params: {"reg_alpha": 0.01, "reg_lambda": 1}, mean: 0.93820, std: 0.01177, params: {"reg_alpha": 0.01, "reg_lambda": 100}, mean: 0.94194, std: 0.00936, params: {"reg_alpha": 0.05, "reg_lambda": 0.01}, mean: 0.94136, std: 0.01122, params: {"reg_alpha": 0.05, "reg_lambda": 0.05}, mean: 0.94164, std: 0.01120...]
參數(shù)的最佳取值：{"reg_alpha": 1, "reg_lambda": 1}
最佳模型得分:0.9441561344357595

由輸出結(jié)果可知參數(shù)的最佳取值：{"reg_alpha": 1, "reg_lambda": 1}。

6、最后就是learning_rate，一般這時(shí)候要調(diào)小學(xué)習(xí)率來測試：

cv_params = {"learning_rate": [0.01, 0.05, 0.07, 0.1, 0.2]}
    other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 4, "min_child_weight": 5, "seed": 0,
                    "subsample": 0.7, "colsample_bytree": 0.7, "gamma": 0.1, "reg_alpha": 1, "reg_lambda": 1}

運(yùn)行后的結(jié)果為：

[Parallel(n_jobs=4)]: Done  25 out of  25 | elapsed:  1.1min finished
每輪迭代運(yùn)行結(jié)果:[mean: 0.93675, std: 0.01080, params: {"learning_rate": 0.01}, mean: 0.94229, std: 0.01138, params: {"learning_rate": 0.05}, mean: 0.94110, std: 0.01066, params: {"learning_rate": 0.07}, mean: 0.94416, std: 0.01037, params: {"learning_rate": 0.1}, mean: 0.93985, std: 0.01109, params: {"learning_rate": 0.2}]
參數(shù)的最佳取值：{"learning_rate": 0.1}
最佳模型得分:0.9441561344357595

由輸出結(jié)果可知參數(shù)的最佳取值：{"learning_rate": 0.1}。

我們可以很清楚地看到，隨著參數(shù)的調(diào)優(yōu)，最佳模型得分是不斷提高的，這也從另一方面驗(yàn)證了調(diào)優(yōu)確實(shí)是起到了一定的作用。不過，我們也可以注意到，其實(shí)最佳分?jǐn)?shù)并沒有提升太多。提醒一點(diǎn)，這個(gè)分?jǐn)?shù)是根據(jù)前面設(shè)置的得分函數(shù)算出來的，即：

optimized_GBM = GridSearchCV(estimator=model, param_grid=cv_params, scoring="r2", cv=5, verbose=1, n_jobs=4)

中的scoring="r2"。在實(shí)際情境中，我們可能需要利用各種不同的得分函數(shù)來評判模型的好壞。

最后，我們把得到的最佳參數(shù)組合扔到模型里訓(xùn)練，就可以得到預(yù)測的結(jié)果了：

def trainandTest(X_train, y_train, X_test):
    # XGBoost訓(xùn)練過程，下面的參數(shù)就是剛才調(diào)試出來的最佳參數(shù)組合
    model = xgb.XGBRegressor(learning_rate=0.1, n_estimators=550, max_depth=4, min_child_weight=5, seed=0,
                             subsample=0.7, colsample_bytree=0.7, gamma=0.1, reg_alpha=1, reg_lambda=1)
    model.fit(X_train, y_train)

    # 對測試集進(jìn)行預(yù)測
    ans = model.predict(X_test)

    ans_len = len(ans)
    id_list = np.arange(10441, 17441)
    data_arr = []
    for row in range(0, ans_len):
        data_arr.append([int(id_list[row]), ans[row]])
    np_data = np.array(data_arr)

    # 寫入文件
    pd_data = pd.DataFrame(np_data, columns=["id", "y"])
    # print(pd_data)
    pd_data.to_csv("submit.csv", index=None)

    # 顯示重要特征
    # plot_importance(model)
    # plt.show()

好了，調(diào)參的過程到這里就基本結(jié)束了。正如我在上面提到的一樣，其實(shí)調(diào)參對于模型準(zhǔn)確率的提高有一定的幫助，但這是有限的。最重要的還是要通過數(shù)據(jù)清洗，特征選擇，特征融合，模型融合等手段來進(jìn)行改進(jìn)！

下面我就貼出完整代碼（聲明一點(diǎn)，我的代碼質(zhì)量不是很好，大家參考一下思路就行）：

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @File  : soccer_value.py
# @Author: Huangqinjian
# @Date  : 2018/3/22
# @Desc  :

import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn import preprocessing
from sklearn import metrics
from sklearn.preprocessing import Imputer
from sklearn.grid_search import GridSearchCV
from hyperopt import hp

# 加載訓(xùn)練數(shù)據(jù)
def featureSet(data):
    imputer = Imputer(missing_values="NaN", strategy="mean", axis=0)
    imputer.fit(data.loc[:, ["rw", "st", "lw", "cf", "cam", "cm"]])
    x_new = imputer.transform(data.loc[:, ["rw", "st", "lw", "cf", "cam", "cm"]])

    le = preprocessing.LabelEncoder()
    le.fit(["Low", "Medium", "High"])
    att_label = le.transform(data.work_rate_att.values)
    # print(att_label)
    def_label = le.transform(data.work_rate_def.values)
    # print(def_label)

    data_num = len(data)
    XList = []
    for row in range(0, data_num):
        tmp_list = []
        tmp_list.append(data.iloc[row]["club"])
        tmp_list.append(data.iloc[row]["league"])
        tmp_list.append(data.iloc[row]["potential"])
        tmp_list.append(data.iloc[row]["international_reputation"])
        tmp_list.append(data.iloc[row]["pac"])
        tmp_list.append(data.iloc[row]["sho"])
        tmp_list.append(data.iloc[row]["pas"])
        tmp_list.append(data.iloc[row]["dri"])
        tmp_list.append(data.iloc[row]["def"])
        tmp_list.append(data.iloc[row]["phy"])
        tmp_list.append(data.iloc[row]["skill_moves"])
        tmp_list.append(x_new[row][0])
        tmp_list.append(x_new[row][1])
        tmp_list.append(x_new[row][2])
        tmp_list.append(x_new[row][3])
        tmp_list.append(x_new[row][4])
        tmp_list.append(x_new[row][5])
        tmp_list.append(att_label[row])
        tmp_list.append(def_label[row])
        XList.append(tmp_list)
    yList = data.y.values
    return XList, yList

# 加載測試數(shù)據(jù)
def loadTestData(filePath):
    data = pd.read_csv(filepath_or_buffer=filePath)
    imputer = Imputer(missing_values="NaN", strategy="mean", axis=0)
    imputer.fit(data.loc[:, ["rw", "st", "lw", "cf", "cam", "cm"]])
    x_new = imputer.transform(data.loc[:, ["rw", "st", "lw", "cf", "cam", "cm"]])

    le = preprocessing.LabelEncoder()
    le.fit(["Low", "Medium", "High"])
    att_label = le.transform(data.work_rate_att.values)
    # print(att_label)
    def_label = le.transform(data.work_rate_def.values)
    # print(def_label)

    data_num = len(data)
    XList = []
    for row in range(0, data_num):
        tmp_list = []
        tmp_list.append(data.iloc[row]["club"])
        tmp_list.append(data.iloc[row]["league"])
        tmp_list.append(data.iloc[row]["potential"])
        tmp_list.append(data.iloc[row]["international_reputation"])
        tmp_list.append(data.iloc[row]["pac"])
        tmp_list.append(data.iloc[row]["sho"])
        tmp_list.append(data.iloc[row]["pas"])
        tmp_list.append(data.iloc[row]["dri"])
        tmp_list.append(data.iloc[row]["def"])
        tmp_list.append(data.iloc[row]["phy"])
        tmp_list.append(data.iloc[row]["skill_moves"])
        tmp_list.append(x_new[row][0])
        tmp_list.append(x_new[row][1])
        tmp_list.append(x_new[row][2])
        tmp_list.append(x_new[row][3])
        tmp_list.append(x_new[row][4])
        tmp_list.append(x_new[row][5])
        tmp_list.append(att_label[row])
        tmp_list.append(def_label[row])
        XList.append(tmp_list)
    return XList


def trainandTest(X_train, y_train, X_test):
    # XGBoost訓(xùn)練過程
    model = xgb.XGBRegressor(learning_rate=0.1, n_estimators=550, max_depth=4, min_child_weight=5, seed=0,
                             subsample=0.7, colsample_bytree=0.7, gamma=0.1, reg_alpha=1, reg_lambda=1)
    model.fit(X_train, y_train)

    # 對測試集進(jìn)行預(yù)測
    ans = model.predict(X_test)

    ans_len = len(ans)
    id_list = np.arange(10441, 17441)
    data_arr = []
    for row in range(0, ans_len):
        data_arr.append([int(id_list[row]), ans[row]])
    np_data = np.array(data_arr)

    # 寫入文件
    pd_data = pd.DataFrame(np_data, columns=["id", "y"])
    # print(pd_data)
    pd_data.to_csv("submit.csv", index=None)

    # 顯示重要特征
    # plot_importance(model)
    # plt.show()


if __name__ == "__main__":
    trainFilePath = "dataset/soccer/train.csv"
    testFilePath = "dataset/soccer/test.csv"
    data = pd.read_csv(trainFilePath)
    X_train, y_train = featureSet(data)
    X_test = loadTestData(testFilePath)
    # 預(yù)測最終的結(jié)果
    # trainandTest(X_train, y_train, X_test)

    """
    下面部分為調(diào)試參數(shù)的代碼
    """

    #
    # cv_params = {"n_estimators": [400, 500, 600, 700, 800]}
    # other_params = {"learning_rate": 0.1, "n_estimators": 500, "max_depth": 5, "min_child_weight": 1, "seed": 0,
    #                 "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0, "reg_alpha": 0, "reg_lambda": 1}
    #
    # cv_params = {"n_estimators": [550, 575, 600, 650, 675]}
    # other_params = {"learning_rate": 0.1, "n_estimators": 600, "max_depth": 5, "min_child_weight": 1, "seed": 0,
    #                 "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0, "reg_alpha": 0, "reg_lambda": 1}
    #
    # cv_params = {"max_depth": [3, 4, 5, 6, 7, 8, 9, 10], "min_child_weight": [1, 2, 3, 4, 5, 6]}
    # other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 5, "min_child_weight": 1, "seed": 0,
    #                 "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0, "reg_alpha": 0, "reg_lambda": 1}
    #
    # cv_params = {"gamma": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]}
    # other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 4, "min_child_weight": 5, "seed": 0,
    #                 "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0, "reg_alpha": 0, "reg_lambda": 1}
    #
    # cv_params = {"subsample": [0.6, 0.7, 0.8, 0.9], "colsample_bytree": [0.6, 0.7, 0.8, 0.9]}
    # other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 4, "min_child_weight": 5, "seed": 0,
    #                 "subsample": 0.8, "colsample_bytree": 0.8, "gamma": 0.1, "reg_alpha": 0, "reg_lambda": 1}
    #
    # cv_params = {"reg_alpha": [0.05, 0.1, 1, 2, 3], "reg_lambda": [0.05, 0.1, 1, 2, 3]}
    # other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 4, "min_child_weight": 5, "seed": 0,
    #                 "subsample": 0.7, "colsample_bytree": 0.7, "gamma": 0.1, "reg_alpha": 0, "reg_lambda": 1}
    #
    # cv_params = {"learning_rate": [0.01, 0.05, 0.07, 0.1, 0.2]}
    # other_params = {"learning_rate": 0.1, "n_estimators": 550, "max_depth": 4, "min_child_weight": 5, "seed": 0,
    #                 "subsample": 0.7, "colsample_bytree": 0.7, "gamma": 0.1, "reg_alpha": 1, "reg_lambda": 1}
    #
    # model = xgb.XGBRegressor(**other_params)
    # optimized_GBM = GridSearchCV(estimator=model, param_grid=cv_params, scoring="r2", cv=5, verbose=1, n_jobs=4)
    # optimized_GBM.fit(X_train, y_train)
    # evalute_result = optimized_GBM.grid_scores_
    # print("每輪迭代運(yùn)行結(jié)果:{0}".format(evalute_result))
    # print("參數(shù)的最佳取值：{0}".format(optimized_GBM.best_params_))
    # print("最佳模型得分:{0}".format(optimized_GBM.best_score_))

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