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【算法竞赛学习】二手车交易价格预测-Task4建模调参

發布時間：2023/12/15 编程问答 25 豆豆

生活随笔收集整理的這篇文章主要介紹了【算法竞赛学习】二手车交易价格预测-Task4建模调参小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

二手車交易價格預測-Task4 建模調參

四、建模與調參

Tip:此部分為零基礎入門數據挖掘的 Task4 建模調參部分，帶你來了解各種模型以及模型的評價和調參策略，歡迎大家后續多多交流。

賽題：零基礎入門數據挖掘 - 二手車交易價格預測

地址：https://tianchi.aliyun.com/competition/entrance/231784/introduction?spm=5176.12281957.1004.1.38b02448ausjSX

5.1 學習目標

了解常用的機器學習模型，并掌握機器學習模型的建模與調參流程
完成相應學習打卡任務

5.2 內容介紹

線性回歸模型：

線性回歸對于特征的要求；
處理長尾分布；
理解線性回歸模型；

模型性能驗證：

評價函數與目標函數；
交叉驗證方法；
留一驗證方法；
針對時間序列問題的驗證；
繪制學習率曲線；
繪制驗證曲線；

嵌入式特征選擇：

Lasso回歸；
Ridge回歸；
決策樹；

模型對比：

常用線性模型；
常用非線性模型；

模型調參：

貪心調參方法；
網格調參方法；
貝葉斯調參方法；

5.3 相關原理介紹與推薦

由于相關算法原理篇幅較長，本文推薦了一些博客與教材供初學者們進行學習。

5.3.1 線性回歸模型

https://zhuanlan.zhihu.com/p/49480391

5.3.2 決策樹模型

https://zhuanlan.zhihu.com/p/65304798

5.3.3 GBDT模型

https://zhuanlan.zhihu.com/p/45145899

5.3.4 XGBoost模型

https://zhuanlan.zhihu.com/p/86816771

5.3.5 LightGBM模型

https://zhuanlan.zhihu.com/p/89360721

5.3.6 推薦教材：

《機器學習》 https://book.douban.com/subject/26708119/
《統計學習方法》 https://book.douban.com/subject/10590856/
《Python大戰機器學習》 https://book.douban.com/subject/26987890/
《面向機器學習的特征工程》 https://book.douban.com/subject/26826639/
《數據科學家訪談錄》 https://book.douban.com/subject/30129410/

5.4 代碼示例

5.4.1 讀取數據

import pandas as pd import numpy as np import warnings warnings.filterwarnings('ignore')

reduce_mem_usage 函數通過調整數據類型，幫助我們減少數據在內存中占用的空間

def reduce_mem_usage(df):""" iterate through all the columns of a dataframe and modify the data typeto reduce memory usage. """start_mem = df.memory_usage().sum() print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))for col in df.columns:col_type = df[col].dtypeif col_type != object:c_min = df[col].min()c_max = df[col].max()if str(col_type)[:3] == 'int':if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:df[col] = df[col].astype(np.int8)elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:df[col] = df[col].astype(np.int16)elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:df[col] = df[col].astype(np.int32)elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:df[col] = df[col].astype(np.int64) else:if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:df[col] = df[col].astype(np.float16)elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:df[col] = df[col].astype(np.float32)else:df[col] = df[col].astype(np.float64)else:df[col] = df[col].astype('category')end_mem = df.memory_usage().sum() print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))return df sample_feature = reduce_mem_usage(pd.read_csv('data_for_tree.csv')) Memory usage of dataframe is 60507328.00 MB Memory usage after optimization is: 15724107.00 MB Decreased by 74.0% continuous_feature_names = [x for x in sample_feature.columns if x not in ['price','brand','model','brand']]

5.4.2 線性回歸 & 五折交叉驗證 & 模擬真實業務情況

sample_feature = sample_feature.dropna().replace('-', 0).reset_index(drop=True) sample_feature['notRepairedDamage'] = sample_feature['notRepairedDamage'].astype(np.float32) train = sample_feature[continuous_feature_names + ['price']]train_X = train[continuous_feature_names] train_y = train['price']

5.4.2 - 1 簡單建模

from sklearn.linear_model import LinearRegression model = LinearRegression(normalize=True) model = model.fit(train_X, train_y)

查看訓練的線性回歸模型的截距（intercept）與權重(coef)

'intercept:'+ str(model.intercept_)sorted(dict(zip(continuous_feature_names, model.coef_)).items(), key=lambda x:x[1], reverse=True) [('v_6', 3342612.384537345),('v_8', 684205.534533214),('v_9', 178967.94192530424),('v_7', 35223.07319016895),('v_5', 21917.550249749802),('v_3', 12782.03250792227),('v_12', 11654.925634146672),('v_13', 9884.194615297649),('v_11', 5519.182176035517),('v_10', 3765.6101415594258),('gearbox', 900.3205339198406),('fuelType', 353.5206495542567),('bodyType', 186.51797317460046),('city', 45.17354204168846),('power', 31.163045441455335),('brand_price_median', 0.535967111869784),('brand_price_std', 0.4346788365040235),('brand_amount', 0.15308295553300566),('brand_price_max', 0.003891831020467389),('seller', -1.2684613466262817e-06),('offerType', -4.759058356285095e-06),('brand_price_sum', -2.2430642281682917e-05),('name', -0.00042591632723759166),('used_time', -0.012574429533889028),('brand_price_average', -0.414105722833381),('brand_price_min', -2.3163823428971835),('train', -5.392535065078232),('power_bin', -59.24591853031839),('v_14', -233.1604256172217),('kilometer', -372.96600915402496),('notRepairedDamage', -449.29703564695365),('v_0', -1490.6790578168238),('v_4', -14219.648899108111),('v_2', -16528.55239086934),('v_1', -42869.43976200439)] from matplotlib import pyplot as plt subsample_index = np.random.randint(low=0, high=len(train_y), size=50)

繪制特征v_9的值與標簽的散點圖，圖片發現模型的預測結果（藍色點）與真實標簽（黑色點）的分布差異較大，且部分預測值出現了小于0的情況，說明我們的模型存在一些問題

plt.scatter(train_X['v_9'][subsample_index], train_y[subsample_index], color='black') plt.scatter(train_X['v_9'][subsample_index], model.predict(train_X.loc[subsample_index]), color='blue') plt.xlabel('v_9') plt.ylabel('price') plt.legend(['True Price','Predicted Price'],loc='upper right') print('The predicted price is obvious different from true price') plt.show() The predicted price is obvious different from true price

通過作圖我們發現數據的標簽（price）呈現長尾分布，不利于我們的建模預測。原因是很多模型都假設數據誤差項符合正態分布，而長尾分布的數據違背了這一假設。參考博客：https://blog.csdn.net/Noob_daniel/article/details/76087829

import seaborn as sns print('It is clear to see the price shows a typical exponential distribution') plt.figure(figsize=(15,5)) plt.subplot(1,2,1) sns.distplot(train_y) plt.subplot(1,2,2) sns.distplot(train_y[train_y < np.quantile(train_y, 0.9)]) It is clear to see the price shows a typical exponential distribution<matplotlib.axes._subplots.AxesSubplot at 0x1b33efb2f98>

在這里我們對標簽進行了 $l o g (x + 1)$ 變換，使標簽貼近于正態分布

train_y_ln = np.log(train_y + 1) import seaborn as sns print('The transformed price seems like normal distribution') plt.figure(figsize=(15,5)) plt.subplot(1,2,1) sns.distplot(train_y_ln) plt.subplot(1,2,2) sns.distplot(train_y_ln[train_y_ln < np.quantile(train_y_ln, 0.9)]) The transformed price seems like normal distribution<matplotlib.axes._subplots.AxesSubplot at 0x1b33f077160>

model = model.fit(train_X, train_y_ln)print('intercept:'+ str(model.intercept_)) sorted(dict(zip(continuous_feature_names, model.coef_)).items(), key=lambda x:x[1], reverse=True) intercept:23.515920686637713[('v_9', 6.043993029165403),('v_12', 2.0357439855551394),('v_11', 1.3607608712255672),('v_1', 1.3079816298861897),('v_13', 1.0788833838535354),('v_3', 0.9895814429387444),('gearbox', 0.009170812023421397),('fuelType', 0.006447089787635784),('bodyType', 0.004815242907679581),('power_bin', 0.003151801949447194),('power', 0.0012550361843629999),('train', 0.0001429273782925814),('brand_price_min', 2.0721302299502698e-05),('brand_price_average', 5.308179717783439e-06),('brand_amount', 2.8308531339942507e-06),('brand_price_max', 6.764442596115763e-07),('offerType', 1.6765966392995324e-10),('seller', 9.308109838457312e-12),('brand_price_sum', -1.3473184925468486e-10),('name', -7.11403461065247e-08),('brand_price_median', -1.7608143661053008e-06),('brand_price_std', -2.7899058266986454e-06),('used_time', -5.6142735899344175e-06),('city', -0.0024992974087053223),('v_14', -0.012754139659375262),('kilometer', -0.013999175312751872),('v_0', -0.04553774829634237),('notRepairedDamage', -0.273686961116076),('v_7', -0.7455902679730504),('v_4', -0.9281349233755761),('v_2', -1.2781892166433606),('v_5', -1.5458846136756323),('v_10', -1.8059217242413748),('v_8', -42.611729973490604),('v_6', -241.30992120503035)]

再次進行可視化，發現預測結果與真實值較為接近，且未出現異常狀況

plt.scatter(train_X['v_9'][subsample_index], train_y[subsample_index], color='black') plt.scatter(train_X['v_9'][subsample_index], np.exp(model.predict(train_X.loc[subsample_index])), color='blue') plt.xlabel('v_9') plt.ylabel('price') plt.legend(['True Price','Predicted Price'],loc='upper right') print('The predicted price seems normal after np.log transforming') plt.show() The predicted price seems normal after np.log transforming

5.4.2 - 2 五折交叉驗證

在使用訓練集對參數進行訓練的時候，經常會發現人們通常會將一整個訓練集分為三個部分（比如mnist手寫訓練集）。一般分為：訓練集（train_set），評估集（valid_set），測試集（test_set）這三個部分。這其實是為了保證訓練效果而特意設置的。其中測試集很好理解，其實就是完全不參與訓練的數據，僅僅用來觀測測試效果的數據。而訓練集和評估集則牽涉到下面的知識了。

因為在實際的訓練中，訓練的結果對于訓練集的擬合程度通常還是挺好的（初始條件敏感），但是對于訓練集之外的數據的擬合程度通常就不那么令人滿意了。因此我們通常并不會把所有的數據集都拿來訓練，而是分出一部分來（這一部分不參加訓練）對訓練集生成的參數進行測試，相對客觀的判斷這些參數對訓練集之外的數據的符合程度。這種思想就稱為交叉驗證（Cross Validation）

from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_absolute_error, make_scorer def log_transfer(func):def wrapper(y, yhat):result = func(np.log(y), np.nan_to_num(np.log(yhat)))return resultreturn wrapper scores = cross_val_score(model, X=train_X, y=train_y, verbose=1, cv = 5, scoring=make_scorer(log_transfer(mean_absolute_error))) [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.1s finished

使用線性回歸模型，對未處理標簽的特征數據進行五折交叉驗證（Error 1.36）

print('AVG:', np.mean(scores)) AVG: 1.3641908155886227

使用線性回歸模型，對處理過標簽的特征數據進行五折交叉驗證（Error 0.19）

scores = cross_val_score(model, X=train_X, y=train_y_ln, verbose=1, cv = 5, scoring=make_scorer(mean_absolute_error)) [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.1s finished print('AVG:', np.mean(scores)) AVG: 0.19382863663604424 scores = pd.DataFrame(scores.reshape(1,-1)) scores.columns = ['cv' + str(x) for x in range(1, 6)] scores.index = ['MAE'] scores cv1cv2cv3cv4cv5MAE

0.191642	0.194986	0.192737	0.195329	0.19445

0.191642

0.194986

0.192737

0.195329

0.19445

5.4.2 - 3 模擬真實業務情況

但在事實上，由于我們并不具有預知未來的能力，五折交叉驗證在某些與時間相關的數據集上反而反映了不真實的情況。通過2018年的二手車價格預測2017年的二手車價格，這顯然是不合理的，因此我們還可以采用時間順序對數據集進行分隔。在本例中，我們選用靠前時間的4/5樣本當作訓練集，靠后時間的1/5當作驗證集，最終結果與五折交叉驗證差距不大

import datetime sample_feature = sample_feature.reset_index(drop=True) split_point = len(sample_feature) // 5 * 4 train = sample_feature.loc[:split_point].dropna() val = sample_feature.loc[split_point:].dropna()train_X = train[continuous_feature_names] train_y_ln = np.log(train['price'] + 1) val_X = val[continuous_feature_names] val_y_ln = np.log(val['price'] + 1) model = model.fit(train_X, train_y_ln) mean_absolute_error(val_y_ln, model.predict(val_X)) 0.19443858353490887

5.4.2 - 4 繪制學習率曲線與驗證曲線

from sklearn.model_selection import learning_curve, validation_curve ? learning_curve def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,n_jobs=1, train_size=np.linspace(.1, 1.0, 5 )): plt.figure() plt.title(title) if ylim is not None: plt.ylim(*ylim) plt.xlabel('Training example') plt.ylabel('score') train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_size, scoring = make_scorer(mean_absolute_error)) train_scores_mean = np.mean(train_scores, axis=1) train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) plt.grid()#區域 plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r") plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g") plt.plot(train_sizes, train_scores_mean, 'o-', color='r', label="Training score") plt.plot(train_sizes, test_scores_mean,'o-',color="g", label="Cross-validation score") plt.legend(loc="best") return plt plot_learning_curve(LinearRegression(), 'Liner_model', train_X[:1000], train_y_ln[:1000], ylim=(0.0, 0.5), cv=5, n_jobs=1) <module 'matplotlib.pyplot' from 'C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py'>

5.4.3 多種模型對比

train = sample_feature[continuous_feature_names + ['price']].dropna()train_X = train[continuous_feature_names] train_y = train['price'] train_y_ln = np.log(train_y + 1)

5.4.3 - 1 線性模型 & 嵌入式特征選擇

本章節默認，學習者已經了解關于過擬合、模型復雜度、正則化等概念。否則請尋找相關資料或參考如下連接：

用簡單易懂的語言描述「過擬合 overfitting」？ https://www.zhihu.com/question/32246256/answer/55320482
模型復雜度與模型的泛化能力 http://yangyingming.com/article/434/
正則化的直觀理解 https://blog.csdn.net/jinping_shi/article/details/52433975

在過濾式和包裹式特征選擇方法中，特征選擇過程與學習器訓練過程有明顯的分別。而嵌入式特征選擇在學習器訓練過程中自動地進行特征選擇。嵌入式選擇最常用的是L1正則化與L2正則化。在對線性回歸模型加入兩種正則化方法后，他們分別變成了嶺回歸與Lasso回歸。

from sklearn.linear_model import LinearRegression from sklearn.linear_model import Ridge from sklearn.linear_model import Lasso models = [LinearRegression(),Ridge(),Lasso()] result = dict() for model in models:model_name = str(model).split('(')[0]scores = cross_val_score(model, X=train_X, y=train_y_ln, verbose=0, cv = 5, scoring=make_scorer(mean_absolute_error))result[model_name] = scoresprint(model_name + ' is finished') LinearRegression is finished Ridge is finished Lasso is finished

對三種方法的效果對比

result = pd.DataFrame(result) result.index = ['cv' + str(x) for x in range(1, 6)] result LinearRegressionRidgeLassocv1cv2cv3cv4cv5

0.191642	0.195665	0.382708
0.194986	0.198841	0.383916
0.192737	0.196629	0.380754
0.195329	0.199255	0.385683
0.194450	0.198173	0.383555

model = LinearRegression().fit(train_X, train_y_ln) print('intercept:'+ str(model.intercept_)) sns.barplot(abs(model.coef_), continuous_feature_names) intercept:23.515984499017883<matplotlib.axes._subplots.AxesSubplot at 0x1feb933ca58>

L2正則化在擬合過程中通常都傾向于讓權值盡可能小，最后構造一個所有參數都比較小的模型。因為一般認為參數值小的模型比較簡單，能適應不同的數據集，也在一定程度上避免了過擬合現象?？梢栽O想一下對于一個線性回歸方程，若參數很大，那么只要數據偏移一點點，就會對結果造成很大的影響；但如果參數足夠小，數據偏移得多一點也不會對結果造成什么影響，專業一點的說法是『抗擾動能力強』

model = Ridge().fit(train_X, train_y_ln) print('intercept:'+ str(model.intercept_)) sns.barplot(abs(model.coef_), continuous_feature_names) intercept:5.901527844424091<matplotlib.axes._subplots.AxesSubplot at 0x1fea9056860>

L1正則化有助于生成一個稀疏權值矩陣，進而可以用于特征選擇。如下圖，我們發現power與userd_time特征非常重要。

model = Lasso().fit(train_X, train_y_ln) print('intercept:'+ str(model.intercept_)) sns.barplot(abs(model.coef_), continuous_feature_names) intercept:8.674427764003347<matplotlib.axes._subplots.AxesSubplot at 0x1fea90b69b0>

除此之外，決策樹通過信息熵或GINI指數選擇分裂節點時，優先選擇的分裂特征也更加重要，這同樣是一種特征選擇的方法。XGBoost與LightGBM模型中的model_importance指標正是基于此計算的

5.4.3 - 2 非線性模型

除了線性模型以外，還有許多我們常用的非線性模型如下，在此篇幅有限不再一一講解原理。我們選擇了部分常用模型與線性模型進行效果比對。

from sklearn.linear_model import LinearRegression from sklearn.svm import SVC from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import GradientBoostingRegressor from sklearn.neural_network import MLPRegressor from xgboost.sklearn import XGBRegressor from lightgbm.sklearn import LGBMRegressor models = [LinearRegression(),DecisionTreeRegressor(),RandomForestRegressor(),GradientBoostingRegressor(),MLPRegressor(solver='lbfgs', max_iter=100), XGBRegressor(n_estimators = 100, objective='reg:squarederror'), LGBMRegressor(n_estimators = 100)] result = dict() for model in models:model_name = str(model).split('(')[0]scores = cross_val_score(model, X=train_X, y=train_y_ln, verbose=0, cv = 5, scoring=make_scorer(mean_absolute_error))result[model_name] = scoresprint(model_name + ' is finished') LinearRegression is finished DecisionTreeRegressor is finished RandomForestRegressor is finished GradientBoostingRegressor is finished MLPRegressor is finished XGBRegressor is finished LGBMRegressor is finished result = pd.DataFrame(result) result.index = ['cv' + str(x) for x in range(1, 6)] result LinearRegressionDecisionTreeRegressorRandomForestRegressorGradientBoostingRegressorMLPRegressorXGBRegressorLGBMRegressorcv1cv2cv3cv4cv5

0.191642	0.184566	0.136266	0.168626	124.299426	0.168698	0.141159
0.194986	0.187029	0.139693	0.171905	257.886236	0.172258	0.143363
0.192737	0.184839	0.136871	0.169553	236.829589	0.168604	0.142137
0.195329	0.182605	0.138689	0.172299	130.197264	0.172474	0.143461
0.194450	0.186626	0.137420	0.171206	268.090236	0.170898	0.141921

可以看到隨機森林模型在每一個fold中均取得了更好的效果

5.4.4 模型調參

在此我們介紹了三種常用的調參方法如下：

貪心算法 https://www.jianshu.com/p/ab89df9759c8
網格調參 https://blog.csdn.net/weixin_43172660/article/details/83032029
貝葉斯調參 https://blog.csdn.net/linxid/article/details/81189154

## LGB的參數集合：objective = ['regression', 'regression_l1', 'mape', 'huber', 'fair']num_leaves = [3,5,10,15,20,40, 55] max_depth = [3,5,10,15,20,40, 55] bagging_fraction = [] feature_fraction = [] drop_rate = []

5.4.4 - 1 貪心調參

best_obj = dict() for obj in objective:model = LGBMRegressor(objective=obj)score = np.mean(cross_val_score(model, X=train_X, y=train_y_ln, verbose=0, cv = 5, scoring=make_scorer(mean_absolute_error)))best_obj[obj] = scorebest_leaves = dict() for leaves in num_leaves:model = LGBMRegressor(objective=min(best_obj.items(), key=lambda x:x[1])[0], num_leaves=leaves)score = np.mean(cross_val_score(model, X=train_X, y=train_y_ln, verbose=0, cv = 5, scoring=make_scorer(mean_absolute_error)))best_leaves[leaves] = scorebest_depth = dict() for depth in max_depth:model = LGBMRegressor(objective=min(best_obj.items(), key=lambda x:x[1])[0],num_leaves=min(best_leaves.items(), key=lambda x:x[1])[0],max_depth=depth)score = np.mean(cross_val_score(model, X=train_X, y=train_y_ln, verbose=0, cv = 5, scoring=make_scorer(mean_absolute_error)))best_depth[depth] = score sns.lineplot(x=['0_initial','1_turning_obj','2_turning_leaves','3_turning_depth'], y=[0.143 ,min(best_obj.values()), min(best_leaves.values()), min(best_depth.values())]) <matplotlib.axes._subplots.AxesSubplot at 0x1fea93f6080>

5.4.4 - 2 Grid Search 調參

from sklearn.model_selection import GridSearchCV parameters = {'objective': objective , 'num_leaves': num_leaves, 'max_depth': max_depth} model = LGBMRegressor() clf = GridSearchCV(model, parameters, cv=5) clf = clf.fit(train_X, train_y) clf.best_params_ {'max_depth': 15, 'num_leaves': 55, 'objective': 'regression'} model = LGBMRegressor(objective='regression',num_leaves=55,max_depth=15) np.mean(cross_val_score(model, X=train_X, y=train_y_ln, verbose=0, cv = 5, scoring=make_scorer(mean_absolute_error))) 0.13626164479243302

5.4.4 - 3 貝葉斯調參

from bayes_opt import BayesianOptimization def rf_cv(num_leaves, max_depth, subsample, min_child_samples):val = cross_val_score(LGBMRegressor(objective = 'regression_l1',num_leaves=int(num_leaves),max_depth=int(max_depth),subsample = subsample,min_child_samples = int(min_child_samples)),X=train_X, y=train_y_ln, verbose=0, cv = 5, scoring=make_scorer(mean_absolute_error)).mean()return 1 - val rf_bo = BayesianOptimization(rf_cv,{'num_leaves': (2, 100),'max_depth': (2, 100),'subsample': (0.1, 1),'min_child_samples' : (2, 100)} ) rf_bo.maximize() | iter | target | max_depth | min_ch... | num_le... | subsample | ------------------------------------------------------------------------- | [0m 1 [0m | [0m 0.8649 [0m | [0m 89.57 [0m | [0m 47.3 [0m | [0m 55.13 [0m | [0m 0.1792 [0m | | [0m 2 [0m | [0m 0.8477 [0m | [0m 99.86 [0m | [0m 60.91 [0m | [0m 15.35 [0m | [0m 0.4716 [0m | | [95m 3 [0m | [95m 0.8698 [0m | [95m 81.74 [0m | [95m 83.32 [0m | [95m 92.59 [0m | [95m 0.9559 [0m | | [0m 4 [0m | [0m 0.8627 [0m | [0m 90.2 [0m | [0m 8.754 [0m | [0m 43.34 [0m | [0m 0.7772 [0m | | [0m 5 [0m | [0m 0.8115 [0m | [0m 10.07 [0m | [0m 86.15 [0m | [0m 4.109 [0m | [0m 0.3416 [0m | | [95m 6 [0m | [95m 0.8701 [0m | [95m 99.15 [0m | [95m 9.158 [0m | [95m 99.47 [0m | [95m 0.494 [0m | | [0m 7 [0m | [0m 0.806 [0m | [0m 2.166 [0m | [0m 2.416 [0m | [0m 97.7 [0m | [0m 0.224 [0m | | [0m 8 [0m | [0m 0.8701 [0m | [0m 98.57 [0m | [0m 97.67 [0m | [0m 99.87 [0m | [0m 0.3703 [0m | | [95m 9 [0m | [95m 0.8703 [0m | [95m 99.87 [0m | [95m 43.03 [0m | [95m 99.72 [0m | [95m 0.9749 [0m | | [0m 10 [0m | [0m 0.869 [0m | [0m 10.31 [0m | [0m 99.63 [0m | [0m 99.34 [0m | [0m 0.2517 [0m | | [95m 11 [0m | [95m 0.8703 [0m | [95m 52.27 [0m | [95m 99.56 [0m | [95m 98.97 [0m | [95m 0.9641 [0m | | [0m 12 [0m | [0m 0.8669 [0m | [0m 99.89 [0m | [0m 8.846 [0m | [0m 66.49 [0m | [0m 0.1437 [0m | | [0m 13 [0m | [0m 0.8702 [0m | [0m 68.13 [0m | [0m 75.28 [0m | [0m 98.71 [0m | [0m 0.153 [0m | | [0m 14 [0m | [0m 0.8695 [0m | [0m 84.13 [0m | [0m 86.48 [0m | [0m 91.9 [0m | [0m 0.7949 [0m | | [0m 15 [0m | [0m 0.8702 [0m | [0m 98.09 [0m | [0m 59.2 [0m | [0m 99.65 [0m | [0m 0.3275 [0m | | [0m 16 [0m | [0m 0.87 [0m | [0m 68.97 [0m | [0m 98.62 [0m | [0m 98.93 [0m | [0m 0.2221 [0m | | [0m 17 [0m | [0m 0.8702 [0m | [0m 99.85 [0m | [0m 63.74 [0m | [0m 99.63 [0m | [0m 0.4137 [0m | | [0m 18 [0m | [0m 0.8703 [0m | [0m 45.87 [0m | [0m 99.05 [0m | [0m 99.89 [0m | [0m 0.3238 [0m | | [0m 19 [0m | [0m 0.8702 [0m | [0m 79.65 [0m | [0m 46.91 [0m | [0m 98.61 [0m | [0m 0.8999 [0m | | [0m 20 [0m | [0m 0.8702 [0m | [0m 99.25 [0m | [0m 36.73 [0m | [0m 99.05 [0m | [0m 0.1262 [0m | | [0m 21 [0m | [0m 0.8702 [0m | [0m 85.51 [0m | [0m 85.34 [0m | [0m 99.77 [0m | [0m 0.8917 [0m | | [0m 22 [0m | [0m 0.8696 [0m | [0m 99.99 [0m | [0m 38.51 [0m | [0m 89.13 [0m | [0m 0.9884 [0m | | [0m 23 [0m | [0m 0.8701 [0m | [0m 63.29 [0m | [0m 97.93 [0m | [0m 99.94 [0m | [0m 0.9585 [0m | | [0m 24 [0m | [0m 0.8702 [0m | [0m 93.04 [0m | [0m 71.42 [0m | [0m 99.94 [0m | [0m 0.9646 [0m | | [0m 25 [0m | [0m 0.8701 [0m | [0m 99.73 [0m | [0m 16.21 [0m | [0m 99.38 [0m | [0m 0.9778 [0m | | [0m 26 [0m | [0m 0.87 [0m | [0m 86.28 [0m | [0m 58.1 [0m | [0m 99.47 [0m | [0m 0.107 [0m | | [0m 27 [0m | [0m 0.8703 [0m | [0m 47.28 [0m | [0m 99.83 [0m | [0m 99.65 [0m | [0m 0.4674 [0m | | [0m 28 [0m | [0m 0.8703 [0m | [0m 68.29 [0m | [0m 99.51 [0m | [0m 99.4 [0m | [0m 0.2757 [0m | | [0m 29 [0m | [0m 0.8701 [0m | [0m 76.49 [0m | [0m 73.41 [0m | [0m 99.86 [0m | [0m 0.9394 [0m | | [0m 30 [0m | [0m 0.8695 [0m | [0m 37.27 [0m | [0m 99.87 [0m | [0m 89.87 [0m | [0m 0.7588 [0m | ========================================================================= 1 - rf_bo.max['target'] 0.1296693644053145

總結

在本章中，我們完成了建模與調參的工作，并對我們的模型進行了驗證。此外，我們還采用了一些基本方法來提高預測的精度，提升如下圖所示。

plt.figure(figsize=(13,5)) sns.lineplot(x=['0_origin','1_log_transfer','2_L1_&_L2','3_change_model','4_parameter_turning'], y=[1.36 ,0.19, 0.19, 0.14, 0.13]) <matplotlib.axes._subplots.AxesSubplot at 0x1feac73ceb8>

總結

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