本文系数据挖掘实战系列文章，我跟大家分享一个数据挖掘实战，与以往的数据实战不同的是，用自动机器学习方法完成模型构建与调优部分工作，深入理解由此带来的便利与效果。
1. Introduction
本文是一篇数据挖掘实战案例，详细探索了从台湾经济杂志收集的1999年到2009年的数据，看看在数据探索过程中，可以洞察出哪些有用的信息，判断哪一个模型能够最准确地预测公司是否破产。
公司破产的定义是根据台湾证券交易所的商业规则而定的。
该建模将尝试使用自动机器学习库pycaret来构建机器学习模型，pycaret是一个用python编写的开源低代码机器学习库，它将机器学习工作流程自动化。如果你想探索这个库并更好地理解它的功能。推荐查看
设置环境并读取数据
python
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import seaborn as sns
bankruptcy_df = pd.read_csv("Bankruptcy.csv")
bankruptcy_df.head() 
技术交流&源码获取
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本文数据&源码，技术交流、按照如下方式获取：
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资料1
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我们打造了《100个超强算法模型》，特点：从0到1轻松学习，原理、代码、案例应有尽有，所有的算法模型都是按照这样的节奏进行表述，所以是一套完完整整的案例库。
很多初学者是有这么一个痛点，就是案例，案例的完整性直接影响同学的兴致。因此，我整理了 100个最常见的算法模型，在你的学习路上助推一把！
2. 理解数据
python
bankruptcy_df.info()
python
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6819 entries, 0 to 6818
Data columns (total 96 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Bankrupt? 6819 non-null int64
1 ROA(C) before interest and depreciation before interest 6819 non-null float64
2 ROA(A) before interest and % after tax 6819 non-null float64
3 ROA(B) before interest and depreciation after tax 6819 non-null float64
4 Operating Gross Margin 6819 non-null float64
5 Realized Sales Gross Margin 6819 non-null float64
6 Operating Profit Rate 6819 non-null float64
7 Pre-tax net Interest Rate 6819 non-null float64
8 After-tax net Interest Rate 6819 non-null float64
9 Non-industry income and expenditure/revenue 6819 non-null float64
10 Continuous interest rate (after tax) 6819 non-null float64
11 Operating Expense Rate 6819 non-null float64
12 Research and development expense rate 6819 non-null float64
13 Cash flow rate 6819 non-null float64
14 Interest-bearing debt interest rate 6819 non-null float64
15 Tax rate (A) 6819 non-null float64
16 Net Value Per Share (B) 6819 non-null float64
17 Net Value Per Share (A) 6819 non-null float64
18 Net Value Per Share (C) 6819 non-null float64
19 Persistent EPS in the Last Four Seasons 6819 non-null float64
20 Cash Flow Per Share 6819 non-null float64
21 Revenue Per Share (Yuan ¥) 6819 non-null float64
22 Operating Profit Per Share (Yuan ¥) 6819 non-null float64
23 Per Share Net profit before tax (Yuan ¥) 6819 non-null float64
24 Realized Sales Gross Profit Growth Rate 6819 non-null float64
25 Operating Profit Growth Rate 6819 non-null float64
26 After-tax Net Profit Growth Rate 6819 non-null float64
27 Regular Net Profit Growth Rate 6819 non-null float64
28 Continuous Net Profit Growth Rate 6819 non-null float64
29 Total Asset Growth Rate 6819 non-null float64
30 Net Value Growth Rate 6819 non-null float64
31 Total Asset Return Growth Rate Ratio 6819 non-null float64
32 Cash Reinvestment % 6819 non-null float64
33 Current Ratio 6819 non-null float64
34 Quick Ratio 6819 non-null float64
35 Interest Expense Ratio 6819 non-null float64
36 Total debt/Total net worth 6819 non-null float64
37 Debt ratio % 6819 non-null float64
38 Net worth/Assets 6819 non-null float64
39 Long-term fund suitability ratio (A) 6819 non-null float64
40 Borrowing dependency 6819 non-null float64
41 Contingent liabilities/Net worth 6819 non-null float64
42 Operating profit/Paid-in capital 6819 non-null float64
43 Net profit before tax/Paid-in capital 6819 non-null float64
44 Inventory and accounts receivable/Net value 6819 non-null float64
45 Total Asset Turnover 6819 non-null float64
46 Accounts Receivable Turnover 6819 non-null float64
47 Average Collection Days 6819 non-null float64
48 Inventory Turnover Rate (times) 6819 non-null float64
49 Fixed Assets Turnover Frequency 6819 non-null float64
50 Net Worth Turnover Rate (times) 6819 non-null float64
51 Revenue per person 6819 non-null float64
52 Operating profit per person 6819 non-null float64
53 Allocation rate per person 6819 non-null float64
54 Working Capital to Total Assets 6819 non-null float64
55 Quick Assets/Total Assets 6819 non-null float64
56 Current Assets/Total Assets 6819 non-null float64
57 Cash/Total Assets 6819 non-null float64
58 Quick Assets/Current Liability 6819 non-null float64
59 Cash/Current Liability 6819 non-null float64
60 Current Liability to Assets 6819 non-null float64
61 Operating Funds to Liability 6819 non-null float64
62 Inventory/Working Capital 6819 non-null float64
63 Inventory/Current Liability 6819 non-null float64
64 Current Liabilities/Liability 6819 non-null float64
65 Working Capital/Equity 6819 non-null float64
66 Current Liabilities/Equity 6819 non-null float64
67 Long-term Liability to Current Assets 6819 non-null float64
68 Retained Earnings to Total Assets 6819 non-null float64
69 Total income/Total expense 6819 non-null float64
70 Total expense/Assets 6819 non-null float64
71 Current Asset Turnover Rate 6819 non-null float64
72 Quick Asset Turnover Rate 6819 non-null float64
73 Working capitcal Turnover Rate 6819 non-null float64
74 Cash Turnover Rate 6819 non-null float64
75 Cash Flow to Sales 6819 non-null float64
76 Fixed Assets to Assets 6819 non-null float64
77 Current Liability to Liability 6819 non-null float64
78 Current Liability to Equity 6819 non-null float64
79 Equity to Long-term Liability 6819 non-null float64
80 Cash Flow to Total Assets 6819 non-null float64
81 Cash Flow to Liability 6819 non-null float64
82 CFO to Assets 6819 non-null float64
83 Cash Flow to Equity 6819 non-null float64
84 Current Liability to Current Assets 6819 non-null float64
85 Liability-Assets Flag 6819 non-null int64
86 Net Income to Total Assets 6819 non-null float64
87 Total assets to GNP price 6819 non-null float64
88 No-credit Interval 6819 non-null float64
89 Gross Profit to Sales 6819 non-null float64
90 Net Income to Stockholder's Equity 6819 non-null float64
91 Liability to Equity 6819 non-null float64
92 Degree of Financial Leverage (DFL) 6819 non-null float64
93 Interest Coverage Ratio (Interest expense to EBIT) 6819 non-null float64
94 Net Income Flag 6819 non-null int64
95 Equity to Liability 6819 non-null float64
dtypes: float64(93), int64(3)
memory usage: 5.0 MB
python
bankruptcy_df.shape
python
(6819, 96)
python
bankruptcy_df.describe() 
3. 数据探索与清洗
3.1 缺失值处理
python
bankruptcy_df.columns[bankruptcy_df.isna().any()]
python
Index([], dtype='object') 从结果看，改数据集非常完整，没有缺失值！
.any()指的是有没有(缺失值)，而与之对应的.all()指的是是否都是(缺失值)
调整数据列名
python
def clean_col_names(col_name):
col_name = (
col_name.strip()
.replace("?", "_")
.replace("(", "_")
.replace(")", "_")
.replace(" ", "_")
.replace("/", "_")
.replace("-", "_")
.replace("__", "_")
.replace("'", "")
.lower()
)
return col_name
bank_columns = list(bankruptcy_df.columns)
bank_columns = [clean_col_names(col_name) for col_name in bank_columns]
bankruptcy_df.columns = bank_columns
display(bankruptcy_df.columns)
python
Index(['bankrupt_', 'roa_c_before_interest_and_depreciation_before_interest',
'roa_a_before_interest_and_%_after_tax',
'roa_b_before_interest_and_depreciation_after_tax',
'operating_gross_margin', 'realized_sales_gross_margin',
'operating_profit_rate', 'pre_tax_net_interest_rate',
'after_tax_net_interest_rate',
'non_industry_income_and_expenditure_revenue',
'continuous_interest_rate_after_tax_', 'operating_expense_rate',
'research_and_development_expense_rate', 'cash_flow_rate',
'interest_bearing_debt_interest_rate', 'tax_rate_a_',
'net_value_per_share_b_', 'net_value_per_share_a_',
'net_value_per_share_c_', 'persistent_eps_in_the_last_four_seasons',
'cash_flow_per_share', 'revenue_per_share_yuan_¥_',
'operating_profit_per_share_yuan_¥_',
'per_share_net_profit_before_tax_yuan_¥_',
'realized_sales_gross_profit_growth_rate',
'operating_profit_growth_rate', 'after_tax_net_profit_growth_rate',
'regular_net_profit_growth_rate', 'continuous_net_profit_growth_rate',
'total_asset_growth_rate', 'net_value_growth_rate',
'total_asset_return_growth_rate_ratio', 'cash_reinvestment_%',
'current_ratio', 'quick_ratio', 'interest_expense_ratio',
'total_debt_total_net_worth', 'debt_ratio_%', 'net_worth_assets',
'long_term_fund_suitability_ratio_a_', 'borrowing_dependency',
'contingent_liabilities_net_worth', 'operating_profit_paid_in_capital',
'net_profit_before_tax_paid_in_capital',
'inventory_and_accounts_receivable_net_value', 'total_asset_turnover',
'accounts_receivable_turnover', 'average_collection_days',
'inventory_turnover_rate_times_', 'fixed_assets_turnover_frequency',
'net_worth_turnover_rate_times_', 'revenue_per_person',
'operating_profit_per_person', 'allocation_rate_per_person',
'working_capital_to_total_assets', 'quick_assets_total_assets',
'current_assets_total_assets', 'cash_total_assets',
'quick_assets_current_liability', 'cash_current_liability',
'current_liability_to_assets', 'operating_funds_to_liability',
'inventory_working_capital', 'inventory_current_liability',
'current_liabilities_liability', 'working_capital_equity',
'current_liabilities_equity', 'long_term_liability_to_current_assets',
'retained_earnings_to_total_assets', 'total_income_total_expense',
'total_expense_assets', 'current_asset_turnover_rate',
'quick_asset_turnover_rate', 'working_capitcal_turnover_rate',
'cash_turnover_rate', 'cash_flow_to_sales', 'fixed_assets_to_assets',
'current_liability_to_liability', 'current_liability_to_equity',
'equity_to_long_term_liability', 'cash_flow_to_total_assets',
'cash_flow_to_liability', 'cfo_to_assets', 'cash_flow_to_equity',
'current_liability_to_current_assets', 'liability_assets_flag',
'net_income_to_total_assets', 'total_assets_to_gnp_price',
'no_credit_interval', 'gross_profit_to_sales',
'net_income_to_stockholders_equity', 'liability_to_equity',
'degree_of_financial_leverage_dfl_',
'interest_coverage_ratio_interest_expense_to_ebit_', 'net_income_flag',
'equity_to_liability'],
dtype='object')统计并绘制目标变量
该步骤的目的是查看目标变量是否平衡，如果不平衡，则需要针对性处理。
python
class_bar=sns.countplot(data=bankruptcy_df,x="bankrupt_")
ax = plt.gca()
for p in ax.patches:
ax.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.3, p.get_height()+500))
class_bar 3.2 特征分布
检查偏态
python
# Return true/false if skewed
import scipy.stats
skew_df = pd.DataFrame(bankruptcy_df.select_dtypes(np.number).columns, columns = ['Feature'])
skew_df['Skew'] = skew_df['Feature'].apply(lambda feature: scipy.stats.skew(bankruptcy_df[feature]))
skew_df['Absolute Skew'] = skew_df['Skew'].apply(abs)
# 得到与方向无关的倾斜幅度
skew_df['Skewed']= skew_df['Absolute Skew'].apply(lambda x: True if x>= 0.5 else False)
with pd.option_context("display.max_rows", 1000):
display(skew_df)
可视化分布
python
cols = list(bankruptcy_df.columns)
ncols = 8
nrows = math.ceil(len(cols) / ncols)
fig, ax = plt.subplots(nrows, ncols, figsize = (4.5 * ncols, 4 * nrows))
for i in range(len(cols)):
sns.kdeplot(bankruptcy_df[cols[i]], ax = ax[i // ncols, i % ncols])
if i % ncols != 0:
ax[i // ncols, i % ncols].set_ylabel(" ")
plt.tight_layout()
plt.show() 
查看有偏态的特征
python
query_skew=skew_df.query("Skewed == True")["Feature"]
with pd.option_context("display.max_rows", 1000):
display(query_skew)
python
0 bankrupt_
2 roa_a_before_interest_and_%_after_tax
3 roa_b_before_interest_and_depreciation_after_tax
4 operating_gross_margin
5 realized_sales_gross_margin
6 operating_profit_rate
7 pre_tax_net_interest_rate
8 after_tax_net_interest_rate
9 non_industry_income_and_expenditure_revenue
10 continuous_interest_rate_after_tax_
11 operating_expense_rate
12 research_and_development_expense_rate
13 cash_flow_rate
14 interest_bearing_debt_interest_rate
15 tax_rate_a_
16 net_value_per_share_b_
17 net_value_per_share_a_
18 net_value_per_share_c_
19 persistent_eps_in_the_last_four_seasons
20 cash_flow_per_share
21 revenue_per_share_yuan_¥_
22 operating_profit_per_share_yuan_¥_
23 per_share_net_profit_before_tax_yuan_¥_
24 realized_sales_gross_profit_growth_rate
25 operating_profit_growth_rate
26 after_tax_net_profit_growth_rate
27 regular_net_profit_growth_rate
28 continuous_net_profit_growth_rate
29 total_asset_growth_rate
30 net_value_growth_rate
31 total_asset_return_growth_rate_ratio
32 cash_reinvestment_%
33 current_ratio
34 quick_ratio
35 interest_expense_ratio
36 total_debt_total_net_worth
37 debt_ratio_%
38 net_worth_assets
39 long_term_fund_suitability_ratio_a_
40 borrowing_dependency
41 contingent_liabilities_net_worth
42 operating_profit_paid_in_capital
43 net_profit_before_tax_paid_in_capital
44 inventory_and_accounts_receivable_net_value
45 total_asset_turnover
46 accounts_receivable_turnover
47 average_collection_days
48 inventory_turnover_rate_times_
49 fixed_assets_turnover_frequency
50 net_worth_turnover_rate_times_
51 revenue_per_person
52 operating_profit_per_person
53 allocation_rate_per_person
57 cash_total_assets
58 quick_assets_current_liability
59 cash_current_liability
60 current_liability_to_assets
61 operating_funds_to_liability
62 inventory_working_capital
63 inventory_current_liability
64 current_liabilities_liability
65 working_capital_equity
66 current_liabilities_equity
67 long_term_liability_to_current_assets
68 retained_earnings_to_total_assets
69 total_income_total_expense
70 total_expense_assets
71 current_asset_turnover_rate
72 quick_asset_turnover_rate
73 working_capitcal_turnover_rate
74 cash_turnover_rate
75 cash_flow_to_sales
76 fixed_assets_to_assets
77 current_liability_to_liability
78 current_liability_to_equity
79 equity_to_long_term_liability
81 cash_flow_to_liability
83 cash_flow_to_equity
84 current_liability_to_current_assets
85 liability_assets_flag
86 net_income_to_total_assets
87 total_assets_to_gnp_price
88 no_credit_interval
89 gross_profit_to_sales
90 net_income_to_stockholders_equity
91 liability_to_equity
92 degree_of_financial_leverage_dfl_
93 interest_coverage_ratio_interest_expense_to_ebit_
95 equity_to_liability
Name: Feature, dtype: object进行下采样，直至样本集中的破产与非破产比例为50/50。完成之后再次对数据进行偏态检查，决定是否需要做log转换，另外进行相关矩阵分析。
3.3 下采样
首先对数据集进行下采样，目标比例为bankrupt vs non bankrupt = 50 vs 50。
python
bankruptcy_df2 = bankruptcy_df.sample(frac=1) #Shuffle Bankruptcy df
bankruptcy_df_b = bankruptcy_df2.loc[bankruptcy_df2["bankrupt_"] == 1]
bankruptcy_df_nb = bankruptcy_df2.loc[bankruptcy_df2["bankrupt_"] == 0][:220]
bankruptcy_subdf_comb = pd.concat([bankruptcy_df_b,bankruptcy_df_nb])
bankruptcy_subdf = bankruptcy_subdf_comb.sample(frac=1,random_state=42)
bankruptcy_subdf 
再次绘图查看正负样本数。
python
sns.countplot(bankruptcy_subdf["bankrupt_"]) 随机选择220家非破产公司和220家破产公司。
4. 特征工程
python
bankruptcy_subdf2 = bankruptcy_subdf.drop(["net_income_flag"],axis=1)
bankruptcy_subdf2.shape
python
(440, 95) 4.1 相关矩阵
python
fig = plt.figure(figsize=(30,20))
ax1 = fig.add_subplot(1,1,1)
sns.heatmap(bankruptcy_subdf2.corr(),ax=ax1,cmap="coolwarm") 4.1.1 找出与破产相关的最高特征
根据对破产企业的基本认识，破产企业资产少、负债高、盈利能力低、现金流少。可以朝这个方向分析我们的数据集。
python
corr=bankruptcy_subdf2[bankruptcy_subdf2.columns[:-1]].corr()['bankrupt_'][:]
corr_df = pd.DataFrame(corr)
print("Correlations to Bankruptcy:")
for index, row in corr_df["bankrupt_"].iteritems():
if row!=1.0 and row>=0.5:
print(f'Positive Correlation: {index}')
elif row!=1.0 and row<=-0.5:
print(f'Negative Correlation: {index}')
python
Correlations to Bankruptcy:
Negative Correlation: roa_c_before_interest_and_depreciation_before_interest
Negative Correlation: roa_b_before_interest_and_depreciation_after_tax
Negative Correlation: net_value_per_share_b_
Negative Correlation: net_value_per_share_a_
Negative Correlation: net_value_per_share_c_
Negative Correlation: persistent_eps_in_the_last_four_seasons
Negative Correlation: per_share_net_profit_before_tax_yuan_¥_
Positive Correlation: debt_ratio_%
Negative Correlation: net_worth_assets
Negative Correlation: net_profit_before_tax_paid_in_capital
Negative Correlation: total_income_total_expense 这些特征代表什么
-
roa_c_before_interest_and_depreciation_before_interest息前资产收益率和息前折旧：总资产收益率--如果总资产收益率低，破产风险高
-
roa_a_before_interest_and_after_tax息前和税后利润：总资产回报率--如果总资产回报率较低，破产风险较高
-
roa_b_before_interest_and_depreciation_after_tax利润不计利息及税后折旧：总资产回报率--如果总资产回报率较低，破产风险较高
-
debt_ratio负债率：负债占总资产的比例--价值越高，负债占资产的比例越高，导致破产风险越高
-
net_worth_assets净资产：净资产越少，破产风险越高
-
retained_earnings_to_total_assets留存收益与总资产之比：留存收益越少，破产风险越高
-
total_income_total_expense总费用：收入与费用之比较低，破产风险较高
-
net_income_to_total_assets净收入与总资产之比：净收入越低，破产风险越高
从结果看，导致公司违约风险越高的特征，似乎与背景知识一致。
4.2 下采样后特征分布可视化
python
# Visualisation of distributions after sub-sampling
cols = list(bankruptcy_subdf2.columns)
ncols = 8
nrows = math.ceil(len(cols) / ncols)
fig, ax = plt.subplots(nrows, ncols, figsize = (4.5 * ncols, 4 * nrows))
for i in range(len(cols)):
sns.kdeplot(bankruptcy_subdf2[cols[i]], ax = ax[i // ncols, i % ncols])
if i % ncols != 0:
ax[i // ncols, i % ncols].set_ylabel(" ")
plt.tight_layout()
plt.show() 
4.3 所有特征的箱线图
python
plt.figure(figsize=(30,20))
boxplot=sns.boxplot(data=bankruptcy_subdf2,orient="h")
boxplot.set(xscale="log")
plt.show() 
4.4 异常值处理
python
quartile1 = bankruptcy_subdf2.quantile(q=0.25,axis=0)
# display(quartile1)
quartile3 = bankruptcy_subdf2.quantile(q=0.75,axis=0)
# display(quartile3)
IQR = quartile3 -quartile1
lower_limit = quartile1-1.5*IQR
upper_limit = quartile3+1.5*IQR
lower_limit = lower_limit.drop(["bankrupt_"])
upper_limit = upper_limit.drop(["bankrupt_"])
# print(lower_limit)
# print(" ")
# print(upper_limit)
bankruptcy_subdf2_out = bankruptcy_subdf2[((bankruptcy_subdf2<lower_limit) | (bankruptcy_subdf2>upper_limit)).any(axis=1)]
display(bankruptcy_subdf2_out.shape)
display(bankruptcy_subdf2.shape)
python
(423, 95)
(440, 95) 额外复制一份表，供后续分析处理。
python
bankruptcy_subdf3 = bankruptcy_subdf2_out.copy()
bankruptcy_subdf3 
下采样后且去除离群值后的分布可视化。
python
# Visualisation of distributions after sub-sampling after outlier removal
cols = list(bankruptcy_subdf3.columns)
ncols = 8
nrows = math.ceil(len(cols) / ncols)
fig, ax = plt.subplots(nrows, ncols, figsize = (4.5 * ncols, 4 * nrows))
for i in range(len(cols)):
sns.kdeplot(bankruptcy_subdf3[cols[i]], ax = ax[i // ncols, i % ncols],fill=True,color="red")
sns.kdeplot(bankruptcy_subdf2[cols[i]], ax = ax[i // ncols, i % ncols],color="green")
if i % ncols != 0:
ax[i // ncols, i % ncols].set_ylabel(" ")
plt.tight_layout()
plt.show() 
5 数据预处理
5.1 特征编码
所有类别在基础数据中都已编码完成，因此这里不需要再次编码列。在实际工作中，这一步大概率是必不可少的，编码技术也是尤其重要，需要好好掌握。如果你还不了解或不是很了解，推荐查看：
5.2 Log转换
这一步是为了去除数据中的偏态分布。
python
# Log transform to remove skews
target = bankruptcy_subdf3['bankrupt_']
bankruptcy_subdf4 = bankruptcy_subdf3.drop(["bankrupt_"],axis=1)
def log_trans(data):
for col in data:
skew = data[col].skew()
if skew>=0.5 or skew<=0.5:
data[col] = np.log1p(data[col])
else:
continue
return data
bankruptcy_subdf4_log = log_trans(bankruptcy_subdf4)
bankruptcy_subdf4_log.head() 
5.2.1 Log转换数据的箱线图
python
plt.figure(figsize=(30,20))
boxplot=sns.boxplot(data=bankruptcy_subdf4_log,orient="h")
boxplot.set(xscale="log")
plt.show() 
5.2.2 Log转换后的数据分布可视化
python
# 在下采样后、去除离群值及log变换后的数据分布的可视化
compare_subdf2 = bankruptcy_subdf2.drop(["bankrupt_"],axis=1)
cols = list(bankruptcy_subdf4.columns)
ncols = 8
nrows = math.ceil(len(cols) / ncols)
fig, ax = plt.subplots(nrows, ncols, figsize = (4.5 * ncols, 4 * nrows))
for i in range(len(cols)):
sns.kdeplot(bankruptcy_subdf4_log[cols[i]], ax = ax[i // ncols, i % ncols],fill=True,color="red")
sns.kdeplot(bankruptcy_subdf2[cols[i]], ax = ax[i // ncols, i % ncols],color="green")
if i % ncols != 0:
ax[i // ncols, i % ncols].set_ylabel(" ")
plt.tight_layout()
plt.show()
print("Red represents distributions after log transforms, green represents before log transform") 
红色表示Log变换后的分布，绿色表示Log变换前的分布。（完整数据集：关注@公众号：数据STUDIO，联系云朵君获取）
6 使用Pycaret构建模型
本次模型构建使用的是自动机器学习框架pycaret，如果你还没有安装，可使用下述命令安装即可。
python
pip install -U --ignore-installed --pre pycaret 
在pycaret中自动完成训练及测试数据的切分工作。
python
from pycaret.classification import *
exp_name = setup(data = bankruptcy_subdf4, target = bankruptcy_subdf3["bankrupt_"]) 
python
compare_models() 
Pycaret显示，3种模型的准确性最高的是
-
LightGBM分类器
-
梯度提升GBC分类器
-
XGBoost分类器
接下来将使用这5个模型进行超参数调优。
6.1 选定模型交叉验证
LightGBM
python
print("LGBM Model")
lgb_clf = create_model("lightgbm")
lgb_clf_scoregrid = pull()
python
LGBM Model 
GBC
python
print("GBC Model")
gbc_clf = create_model("gbc")
gbc_clf_scoregrid = pull() GBC Model
XGBoost
python
print("XGB Model")
xgb_clf = create_model("xgboost")
xgb_clf_scoregrid = pull()
python
XGB Model 
7 使用Pycaret进行超参数调优
7.1 模型调优
LightGBM
python
print("Before Tuning")
print(lgb_clf_scoregrid.loc[["Mean","Std"]])
print("")
lgb_clf = tune_model(lgb_clf,choose_better=True)
print(lgb_clf)
python
Before Tuning
Accuracy AUC Recall Prec. F1 Kappa MCC
Fold
Mean 0.8433 0.9233 0.8562 0.8497 0.8495 0.6866 0.6929
Std 0.0524 0.0429 0.0802 0.0681 0.0506 0.1046 0.1048 
GBC
python
print("Before Tuning")
print(gbc_clf_scoregrid.loc[["Mean","Std"]])
print("")
gbc_clf = tune_model(gbc_clf,choose_better=True)
print(gbc_clf)
python
Before Tuning
Accuracy AUC Recall Prec. F1 Kappa MCC
Fold
Mean 0.8329 0.9242 0.8558 0.8324 0.8419 0.6649 0.6691
Std 0.0599 0.0403 0.0634 0.0750 0.0557 0.1204 0.1198 
XGBoost
python
print("Before Tuning")
print(xgb_clf_scoregrid.loc[["Mean","Std"]])
print("")
xgb_clf = tune_model(xgb_clf,choose_better = True)
print(xgb_clf)
python
Before Tuning
Accuracy AUC Recall Prec. F1 Kappa MCC
Fold
Mean 0.8400 0.9270 0.8562 0.8410 0.8460 0.6797 0.6852
Std 0.0582 0.0382 0.0906 0.0586 0.0583 0.1161 0.1187 
7.2 模型集成
-
Bagged & Boosting 方法
-
Blending
-
Stacking
LightGBM
python
# Original
print(lgb_clf_scoregrid.loc[['Mean', 'Std']])
# Compare the original against bagged and boosted
# Bagged
lgb_clf = ensemble_model(lgb_clf,fold =5,choose_better = True)
# Boosted
lgb_clf = ensemble_model(lgb_clf,method="Boosting",choose_better = True)
python
Accuracy AUC Recall Prec. F1 Kappa MCC
Fold
Mean 0.8433 0.9233 0.8562 0.8497 0.8495 0.6866 0.6929
Std 0.0524 0.0429 0.0802 0.0681 0.0506 0.1046 0.1048
GBC
python
# Original
print(gbc_clf_scoregrid.loc[['Mean', 'Std']])
# Compare the original against bagged and boosted
# Bagged
gbc_clf = ensemble_model(gbc_clf,fold =5,choose_better = True)
# Boosted
gbc_clf = ensemble_model(gbc_clf,method="Boosting",choose_better = True) Accuracy AUC Recall Prec. F1 Kappa MCC
Fold
Mean 0.8329 0.9242 0.8558 0.8324 0.8419 0.6649 0.6691
Std 0.0599 0.0403 0.0634 0.0750 0.0557 0.1204 0.1198
XGBoost
python
# Original
print(xgb_clf_scoregrid.loc[['Mean', 'Std']])
# Compare the original and boosted against bagged and boosted
# Bagged
xgb_clf = ensemble_model(xgb_clf,fold =5,choose_better = True)
# Boosted
xgb_clf = ensemble_model(xgb_clf,method="Boosting",choose_better = True)
python
Accuracy AUC Recall Prec. F1 Kappa MCC
Fold
Mean 0.8400 0.9270 0.8562 0.8410 0.8460 0.6797 0.6852
Std 0.0582 0.0382 0.0906 0.0586 0.0583 0.1161 0.1187
7.3.1 Blend Models
python
blend_models([lgb_clf, gbc_clf, xgb_clf],choose_better=True) 
7.3.2 Stacking
python
stacker = stack_models(lgb_clf,gbc_clf) #remove xgb as some issues 
python
print(stacker) 8 模型评估
python
# evaluate_model(lgb_clf)
# evaluate_model(gbc_clf)
# evaluate_model(xgb_clf) 8.1 ROC-AUC
python
plot_model(stacker, plot = 'auc')
# Stacked classifier from ensembling
plot_model(lgb_clf, plot = 'auc')
# lgb最适合Bagging集成并被选中
plot_model(gbc_clf, plot = 'auc')
# gbc最适合Boosting集成并被选中
plot_model(xgb_clf, plot = 'auc')
# 基本的xgb分类器在经过调优和集成后仍然表现最好，因此选择了它 
8.2 混淆矩阵
python
plot_model(stacker,
plot = 'confusion_matrix',
plot_kwargs = {'percent' : True})
plot_model(lgb_clf,
plot = 'confusion_matrix',
plot_kwargs = {'percent' : True})
plot_model(gbc_clf,
plot = 'confusion_matrix',
plot_kwargs = {'percent' : True})
plot_model(xgb_clf,
plot = 'confusion_matrix',
plot_kwargs = {'percent' : True}) 
8.3 学习曲线
python
plot_model(stacker, plot = 'learning') 
python
plot_model(lgb_clf, plot = 'learning') 
就到这里了！