系統環境：
- 操作系統：Windows 8.1 64-bit
- CPU：Intel i7-4200HQ 3.60GHz
- RAM：8GB
- GPU: GeForce GTX 970M (CUDA 10.1)
庫環境：
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
import sklearn
print('pandas version：',pd.__version__)
print('matplotlib version：',matplotlib.__version__)
print('seaborn version：',sns.__version__)
print('sklearn version：',sklearn.__version__)
pandas version： 0.23.4
matplotlib version： 2.2.3
seaborn version： 0.9.0
sklearn version： 0.22.2.post1
讀取數據
- 數據下載地址：kaggle_Click-Through Rate Prediction
- 由於數據量大，筆記本內存有限，無法讀取和運算如此大量的數據。因此使用dump_data.py進行採樣，選取10000條數據進行訓練，10000條數據進行測試
File descriptions：
- train - Training set. 10 days of click-through data, ordered chronologically. Non-clicks and clicks are subsampled according to different strategies.
- test - Testing set. 1 day of ads to for testing your model predictions.
Data fields：
- id: ad identifier
- click: 0/1 for non-click/click
- hour: format is YYMMDDHH, so 14091123 means 23:00 on Sept. 11, 2014 UTC.
- C1 – anonymized categorical variable
- banner_pos
- site_id
- site_domain
- site_category
- app_id
- app_domain
- app_category
- device_id
- device_ip
- device_model
- device_type
- device_conn_type
- C14-C21 – anonymized categorical variables
data_train = pd.read_csv(r'./dataset/ctr/train_sample_ctr_8450.csv')
data_test = pd.read_csv(r'.\dataset\ctr\test_sample_ctr_155.csv')
data_train.info()
data_test.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8450 entries, 0 to 8449
Data columns (total 24 columns):
id 8450 non-null uint64
click 8450 non-null int64
hour 8450 non-null int64
C1 8450 non-null int64
banner_pos 8450 non-null int64
site_id 8450 non-null object
site_domain 8450 non-null object
site_category 8450 non-null object
app_id 8450 non-null object
app_domain 8450 non-null object
app_category 8450 non-null object
device_id 8450 non-null object
device_ip 8450 non-null object
device_model 8450 non-null object
device_type 8450 non-null int64
device_conn_type 8450 non-null int64
C14 8450 non-null int64
C15 8450 non-null int64
C16 8450 non-null int64
C17 8450 non-null int64
C18 8450 non-null int64
C19 8450 non-null int64
C20 8450 non-null int64
C21 8450 non-null int64
dtypes: int64(14), object(9), uint64(1)
memory usage: 1.5+ MB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 155 entries, 0 to 154
Data columns (total 23 columns):
id 155 non-null uint64
hour 155 non-null int64
C1 155 non-null int64
banner_pos 155 non-null int64
site_id 155 non-null object
site_domain 155 non-null object
site_category 155 non-null object
app_id 155 non-null object
app_domain 155 non-null object
app_category 155 non-null object
device_id 155 non-null object
device_ip 155 non-null object
device_model 155 non-null object
device_type 155 non-null int64
device_conn_type 155 non-null int64
C14 155 non-null int64
C15 155 non-null int64
C16 155 non-null int64
C17 155 non-null int64
C18 155 non-null int64
C19 155 non-null int64
C20 155 non-null int64
C21 155 non-null int64
dtypes: int64(13), object(9), uint64(1)
memory usage: 27.9+ KB
data_train.describe()
| id | click | hour | C1 | banner_pos | device_type | device_conn_type | C14 | C15 | C16 | C17 | C18 | C19 | C20 | C21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 8.450000e+03 | 8450.000000 | 8.450000e+03 | 8450.000000 | 8450.000000 | 8450.000000 | 8450.000000 | 8450.000000 | 8450.000000 | 8450.000000 | 8450.000000 | 8450.000000 | 8450.000000 | 8450.000000 | 8450.000000 |
| mean | 9.179895e+18 | 0.177751 | 1.410255e+07 | 1004.959763 | 0.287219 | 1.010178 | 0.346982 | 18833.676805 | 319.027219 | 61.371124 | 2110.164734 | 1.446154 | 222.525680 | 52899.882604 | 83.929231 |
| std | 5.358954e+18 | 0.382326 | 2.981426e+02 | 1.085978 | 0.510966 | 0.514568 | 0.869565 | 5012.525716 | 23.672810 | 52.690929 | 615.540273 | 1.327442 | 343.077724 | 49979.530350 | 70.765039 |
| min | 1.275883e+15 | 0.000000 | 1.410210e+07 | 1001.000000 | 0.000000 | 0.000000 | 0.000000 | 375.000000 | 216.000000 | 36.000000 | 112.000000 | 0.000000 | 33.000000 | -1.000000 | 13.000000 |
| 25% | 4.536772e+18 | 0.000000 | 1.410230e+07 | 1005.000000 | 0.000000 | 1.000000 | 0.000000 | 16920.000000 | 320.000000 | 50.000000 | 1863.000000 | 0.000000 | 35.000000 | -1.000000 | 23.000000 |
| 50% | 9.077211e+18 | 0.000000 | 1.410252e+07 | 1005.000000 | 0.000000 | 1.000000 | 0.000000 | 20352.000000 | 320.000000 | 50.000000 | 2325.000000 | 2.000000 | 39.000000 | 100035.500000 | 61.000000 |
| 75% | 1.377866e+19 | 0.000000 | 1.410281e+07 | 1005.000000 | 1.000000 | 1.000000 | 0.000000 | 21894.000000 | 320.000000 | 50.000000 | 2526.000000 | 3.000000 | 171.000000 | 100103.000000 | 110.000000 |
| max | 1.844664e+19 | 1.000000 | 1.410302e+07 | 1012.000000 | 7.000000 | 5.000000 | 5.000000 | 24041.000000 | 768.000000 | 1024.000000 | 2756.000000 | 3.000000 | 1839.000000 | 100248.000000 | 253.000000 |
data_test.describe()
| id | hour | C1 | banner_pos | device_type | device_conn_type | C14 | C15 | C16 | C17 | C18 | C19 | C20 | C21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1.550000e+02 | 1.550000e+02 | 155.000000 | 155.000000 | 155.000000 | 155.000000 | 155.000000 | 155.000000 | 155.000000 | 155.000000 | 155.000000 | 155.000000 | 155.000000 | 155.000000 |
| mean | 8.928224e+18 | 1.410311e+07 | 1005.051613 | 0.187097 | 1.051613 | 0.380645 | 21478.096774 | 318.941935 | 53.780645 | 2437.064516 | 1.309677 | 153.864516 | 56847.980645 | 92.683871 |
| std | 5.552391e+18 | 5.682679e+00 | 0.938315 | 0.391253 | 0.507010 | 0.913613 | 4764.198700 | 8.769219 | 27.678645 | 607.895654 | 1.331768 | 215.958632 | 49765.040950 | 87.115519 |
| min | 7.968314e+16 | 1.410310e+07 | 1002.000000 | 0.000000 | 0.000000 | 0.000000 | 4687.000000 | 216.000000 | 36.000000 | 423.000000 | 0.000000 | 33.000000 | -1.000000 | 13.000000 |
| 25% | 4.190929e+18 | 1.410311e+07 | 1005.000000 | 0.000000 | 1.000000 | 0.000000 | 22104.000000 | 320.000000 | 50.000000 | 2545.000000 | 0.000000 | 35.000000 | -1.000000 | 23.000000 |
| 50% | 9.154648e+18 | 1.410311e+07 | 1005.000000 | 0.000000 | 1.000000 | 0.000000 | 23141.000000 | 320.000000 | 50.000000 | 2664.000000 | 1.000000 | 39.000000 | 100077.000000 | 51.000000 |
| 75% | 1.394021e+19 | 1.410312e+07 | 1005.000000 | 0.000000 | 1.000000 | 0.000000 | 24095.500000 | 320.000000 | 50.000000 | 2761.000000 | 3.000000 | 175.000000 | 100148.000000 | 221.000000 |
| max | 1.835151e+19 | 1.410312e+07 | 1010.000000 | 1.000000 | 4.000000 | 3.000000 | 24320.000000 | 320.000000 | 250.000000 | 2790.000000 | 3.000000 | 1327.000000 | 100233.000000 | 251.000000 |
觀察上述數據，可以發現數據整體不存在缺失值，數據類型爲int或object, 需要對object類型數據進行向量化/離散化。對於ID這個特徵，可以看到其方差極大，說明是一個連續的數據，結合經驗，可以直接刪除該特徵
數據預處理
- 刪除id特徵
data_train = data_train.drop('id',axis=1)
data_test = data_test.drop('id',axis=1)
- 觀察object類型特徵並進行向量化/離散化處理
df_object = data_train.filter(regex='site_id|site_domain|site_category|app_id|app_domain|app_category|device_id|device_ip|device_model')
for itm, column in enumerate(df_object.columns):
category = df_object.iloc[itm].unique()
num = len(category)
print('{}: {} \n num: {}'.format(column, category, num))
site_id: ['6399eda6' '968765cd' 'f028772b' 'ecad2386' '7801e8d9' '07d7df22'
'a99f214a' 'ffd71479' '76dc4769']
num: 9
site_domain: ['8fda644b' '25d4cfcd' 'f028772b' 'ecad2386' '7801e8d9' '07d7df22'
'a99f214a' '34674ab0' '6569eeb3']
num: 9
site_category: ['5b4d2eda' '16a36ef3' 'f028772b' 'ecad2386' '7801e8d9' '07d7df22'
'a99f214a' 'cdc5cc94' '00b1f3a7']
num: 9
app_id: ['d8bb8687' '98e6755b' '3e814130' 'ecad2386' '7801e8d9' '07d7df22'
'a99f214a' '799d2fa5' 'be6db1d7']
num: 9
app_domain: ['1fbe01fe' 'f3845767' '28905ebd' 'ecad2386' '7801e8d9' '07d7df22'
'a99f214a' '0a945a46' '8a4875bd']
num: 9
app_category: ['83a0ad1a' '5c9ae867' 'f028772b' 'ecad2386' '7801e8d9' '07d7df22'
'a99f214a' '01bd946f' 'c48dce72']
num: 9
device_id: ['85f751fd' 'c4e18dd6' '50e219e0' 'e2a1ca37' '2347f47a' '8ded1f7a'
'324d6925' '51b00dde' 'dc70b0f9']
num: 9
device_ip: ['1fbe01fe' 'f3845767' '28905ebd' 'ecad2386' '7801e8d9' '07d7df22'
'a99f214a' '2d905ba5' 'c6263d8a']
num: 9
device_model: ['85f751fd' 'c4e18dd6' '50e219e0' '685d1c4c' '2347f47a' '8ded1f7a'
'a99f214a' 'a0ec533d' '88fbaa46']
num: 9
特徵離散/因子化
site_id = pd.get_dummies(data_train['site_id'], prefix= 'site_id')
site_domain = pd.get_dummies(data_train['site_domain'], prefix= 'site_domain')
site_category = pd.get_dummies(data_train['site_category'], prefix= 'site_category')
app_id = pd.get_dummies(data_train['app_id'], prefix= 'app_id')
app_domain = pd.get_dummies(data_train['app_domain'], prefix= 'app_domain')
app_category = pd.get_dummies(data_train['app_category'], prefix= 'app_category')
device_id = pd.get_dummies(data_train['device_id'], prefix= 'device_id')
device_ip = pd.get_dummies(data_train['device_ip'], prefix= 'device_ip')
device_model = pd.get_dummies(data_train['device_model'], prefix= 'device_model')
df = pd.concat([site_id, site_domain, site_category,
app_id, app_domain, app_category,
device_id, device_ip, device_model], axis=1)
df.columns
Index(['site_id_021cd138', 'site_id_02296256', 'site_id_023f3644',
'site_id_0273c5ad', 'site_id_02d5151c', 'site_id_030440fe',
'site_id_05c65e53', 'site_id_06a0ac14', 'site_id_070ca277',
'site_id_079325ff',
...
'device_model_feacaaee', 'device_model_feb70d53',
'device_model_ff065cf0', 'device_model_ff0f1aca',
'device_model_ff16d623', 'device_model_ff2a3543',
'device_model_ff607a1a', 'device_model_ff91ea03',
'device_model_ffc70ef9', 'device_model_ffe69079'],
dtype='object', length=11919)
訓練
小部分特徵訓練
- 只選擇int類型數據進行訓練
def train(train_np):
from sklearn import linear_model
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import time
# y即Survival結果
y = train_np[:, 0]
# X即特徵屬性值
X = train_np[:, 1:]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
clf = linear_model.LogisticRegression(C=1,penalty="l2")
t1 = time.time()
clf.fit(X_train,y_train)
t2 = time.time()
y_pred = clf.predict(X_test)
# print('accuracy:{:.3f}%, tims:{:.3f}s'.format(accuracy_score(y_test, y_pred)*100,(t2-t1)))
return accuracy_score(y_test, y_pred), (t2-t1)
def train_cv(train_np):
from sklearn import linear_model
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
import time
# y即Survival結果
y = train_np[:, 0]
# X即特徵屬性值
X = train_np[:, 1:]
clf = linear_model.LogisticRegression(C=1,penalty="l2")
t1 = time.time()
scores = cross_val_score(clf, X, y, cv=5)
t2 = time.time()
return np.mean(scores), (t2-t1)/5
import time
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(16,9))
sns.heatmap(data_train[["click","hour","banner_pos","C1","C14","C15","C16",
"C17","C18","C19","C20","C21","device_type","device_conn_type"]].corr(),
annot=True, fmt = ".2f", cmap = "coolwarm")
plt.show()
通過上圖可以看出單一數值型的特徵與標籤的線性關係並不是很大，下面看一下訓練的效果
few_feature_df = data_train.filter(regex='click|hour|C.*|banner_pos|device_type|device_conn_type')
print('num of feature:',len(few_feature_df.columns))
few_feature_np = few_feature_df.values
acc, t = train_cv(few_feature_np)
print('accuracy:{:.3f}%, tims:{:.3f}s'.format(acc*100,t))
num of feature: 14
accuracy:82.225%, tims:0.035s
全特徵訓練
- 使用數值類特徵以及特徵離散化過後的特徵（共計：11931個）
all_feature_df = pd.concat([few_feature_df, df], axis=1)
print('num of feature:',len(all_feature_df.columns))
all_feature_np = all_feature_df.values
acc, t = train_cv(all_feature_np)
print('accuracy:{:.3f}%, tims:{:.3f}s'.format(acc*100,t))
num of feature: 11933
accuracy:82.225%, tims:7.074s
選取部分特徵和全特徵進行訓練最終的結果一直，但時間消耗相差數倍。因此我們應當進行特徵選擇
選擇部分特徵
- site_domain, site_category
df_1 = pd.concat([data_train, site_domain, site_category], axis=1)
df_1.drop(['site_domain', 'site_category'], axis=1, inplace=True)
train_df = df_1.filter(regex='click|site_domain_.*|hour|site_category_.*|C.*|banner_pos')
train_np = train_df.values
acc, t = train_cv(train_np)
print('accuracy:{:.3f}%, tims:{:.3f}s'.format(acc*100,t))
accuracy:82.225%, tims:1.448s
看到結果還是沒有什麼太大的變化，通過之前觀察數據，部分特徵的量綱之間差距很大，接下來進行特徵數據標準化，來看一下效果。
標準化
- StandardScaler() → 均值爲0，方差爲1
- MinMaxScaler() → [0, 1]
- MaxAbsScaler() → [-1, 1]
from sklearn.preprocessing import StandardScaler,MinMaxScaler,MaxAbsScaler
import warnings
warnings.filterwarnings('ignore')
scalers = [MinMaxScaler(),
StandardScaler(),
MaxAbsScaler()]
names = ['MinMaxScaler','StandardScaler()','MaxAbsScaler']
for name, scaler in zip(names,scalers):
print(name,':')
temp = scaler.fit_transform(df_1.filter(regex='hour|C.*|banner_pos|device_type|device_conn_type'))
temp = pd.DataFrame(temp,
columns=['hour_s','C1_s','C14_s','C15_s','C16_s','C17_s','C18_s',
'C19_s','C20_s','C21_s','banner_pos_s','device_type_s','device_conn_type_s'])
df_1_s = pd.concat([data_train['click'], temp, site_domain, site_category], axis=1)
acc, t = train_cv(df_1_s.values)
print('accuracy:{:.3f}%, tims:{:.3f}s'.format(acc*100,t))
MinMaxScaler :
accuracy:82.178%, tims:2.935s
StandardScaler() :
accuracy:82.142%, tims:2.540s
MaxAbsScaler :
accuracy:82.166%, tims:2.866s
通過效果看出，對於該問題使用MinMaxScaler()效果比較好，也是因爲其與特徵都進行離散化（[0, 1]）,這樣量綱統一了。
接下來嘗試再增加特徵：
由於ip信息大部分均爲不同的數據，做爲特徵時對結果會產生負影響。因此排除ip類的特徵，選取其與特徵
scaler = MinMaxScaler()
temp = scaler.fit_transform(df_1.filter(regex='hour|C.*|banner_pos|device_type|device_conn_type'))
trian_data_scalered = pd.DataFrame(temp,
columns=['hour_s','C1_s','C14_s','C15_s','C16_s','C17_s','C18_s',
'C19_s','C20_s','C21_s','banner_pos_s','device_type_s','device_conn_type_s'])
selected_feature_df = pd.concat([data_train['click'], trian_data_scalered,site_domain,site_category,
app_domain,app_category,device_model], axis=1)
acc, t = train_cv(selected_feature_df.values)
print('accuracy:{:.3f}%, tims:{:.3f}s'.format(acc*100,t))
accuracy:82.154%, tims:7.976s
- 在嘗試減少特徵：刪去domain類特徵
scaler = MinMaxScaler()
temp = scaler.fit_transform(df_1.filter(regex='hour|C.*|banner_pos|device_type|device_conn_type'))
trian_data_scalered = pd.DataFrame(temp,
columns=['hour_s','C1_s','C14_s','C15_s','C16_s','C17_s','C18_s',
'C19_s','C20_s','C21_s','banner_pos_s','device_type_s','device_conn_type_s'])
selected_feature_df = pd.concat([data_train['click'], trian_data_scalered,site_category,
app_category,device_model], axis=1)
acc, t = train_cv(selected_feature_df.values)
print('accuracy:{:.3f}%, tims:{:.3f}s'.format(acc*100,t))
accuracy:82.059%, tims:1.255s
- 選取site類特徵以及其他特徵，不選取app和device類特徵
scaler = MinMaxScaler()
temp = scaler.fit_transform(df_1.filter(regex='hour|C.*|banner_pos'))
trian_data_scalered = pd.DataFrame(temp,
columns=['hour_s','C1_s','C14_s','C15_s','C16_s','C17_s','C18_s',
'C19_s','C20_s','C21_s','banner_pos_s'])
selected_feature_df = pd.concat([data_train['click'], trian_data_scalered,site_id, site_domain,site_category], axis=1)
acc, t = train_cv(selected_feature_df.values)
print('accuracy:{:.3f}%, tims:{:.3f}s'.format(acc*100,t))
accuracy:82.130%, tims:0.973s
- 選取app類特徵以及其他特徵，不選取site和device類特徵
scaler = MinMaxScaler()
temp = scaler.fit_transform(df_1.filter(regex='hour|C.*|banner_pos'))
trian_data_scalered = pd.DataFrame(temp,
columns=['hour_s','C1_s','C14_s','C15_s','C16_s','C17_s','C18_s',
'C19_s','C20_s','C21_s','banner_pos_s'])
selected_feature_df = pd.concat([data_train['click'], trian_data_scalered,app_id, app_domain, app_category], axis=1)
acc, t = train_cv(selected_feature_df.values)
print('accuracy:{:.3f}%, tims:{:.3f}s'.format(acc*100,t))
accuracy:82.178%, tims:0.546s
- 選取device類特徵以及其他特徵，不選取app和site類特徵
scaler = MinMaxScaler()
temp = scaler.fit_transform(df_1.filter(regex='hour|C.*|banner_pos|device_type|device_conn_type'))
trian_data_scalered = pd.DataFrame(temp,
columns=['hour_s','C1_s','C14_s','C15_s','C16_s','C17_s','C18_s',
'C19_s','C20_s','C21_s','banner_pos_s','device_type_s','device_conn_type_s'])
selected_feature_df = pd.concat([data_train['click'], trian_data_scalered,device_id, device_ip, device_model], axis=1)
acc, t = train_cv(selected_feature_df.values)
print('accuracy:{:.3f}%, tims:{:.3f}s'.format(acc*100,t))
accuracy:82.012%, tims:8.948s
通過特徵選擇，準確率提升的提升並不是很大，因此嘗試通過對邏輯迴歸模型調參
模型調參
from sklearn.model_selection import GridSearchCV
from sklearn import linear_model
# set data
scaler = MinMaxScaler()
temp = scaler.fit_transform(df_1.filter(regex='hour|C.*|banner_pos|device_type|device_conn_type'))
trian_data_scalered = pd.DataFrame(temp,
columns=['hour_s','C1_s','C14_s','C15_s','C16_s','C17_s','C18_s',
'C19_s','C20_s','C21_s','banner_pos_s','device_type_s','device_conn_type_s'])
selected_feature_df = pd.concat([data_train['click'], trian_data_scalered,site_domain,site_category,
app_domain,app_category,device_model], axis=1)
X = selected_feature_df.values[:,1:]
y = selected_feature_df.values[:,0]
# set train model
clf = linear_model.LogisticRegression()
param_LR = {'C':[0.01, 0.1, 0.5, 1, 5, 20, 50],
'penalty':['l1'],
'solver':['liblinear','saga'],
'class_weight':['balanced','None']}
clf_gscv_l1 = GridSearchCV(clf, param_grid = param_LR, cv=5, scoring="accuracy", n_jobs= 8, verbose = 1)
clf_gscv_l1.fit(X, y)
Fitting 5 folds for each of 28 candidates, totalling 140 fits
[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 43.2s
[Parallel(n_jobs=8)]: Done 140 out of 140 | elapsed: 6.2min finished
print(clf_gscv_l1.best_estimator_)
print(clf_gscv_l1.best_score_)
LogisticRegression(C=0.1, class_weight='None', dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l1',
random_state=None, solver='saga', tol=0.0001, verbose=0,
warm_start=False)
0.8223668639053254
clf = linear_model.LogisticRegression()
param_LR = {'C':[0.01, 0.1, 0.5, 1, 5, 20, 50],
'penalty':['l2'],
'solver':['newton-cg','lbfgs','liblinear'],
'class_weight':['balanced','None']}
clf_gscv_l2 = GridSearchCV(clf, param_grid = param_LR, cv=5, scoring="accuracy", n_jobs= 8, verbose = 1)
clf_gscv_l2.fit(X, y)
Fitting 5 folds for each of 42 candidates, totalling 210 fits
[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 27.4s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 4.8min
[Parallel(n_jobs=8)]: Done 210 out of 210 | elapsed: 5.9min finished
print(clf_gscv_l2.best_estimator_)
print(clf_gscv_l2.best_score_)
LogisticRegression(C=0.01, class_weight='None', dual=False, fit_intercept=True,
intercept_scaling=1, l1_ratio=None, max_iter=100,
multi_class='auto', n_jobs=None, penalty='l2',
random_state=None, solver='newton-cg', tol=0.0001, verbose=0,
warm_start=False)
0.8222485207100592
由於邏輯迴歸中L1和L2近適用於不同的優化算法（solver）,所以分開進行調參。因爲數據的標籤是不均衡的，所以在調參中增添了標籤均衡，但由於開啓標籤均衡後會導致訓練集準確率下降，所以這裏最佳參數中class_weight=None。但爲了預防過擬合的情況，還需將將參數class_weight=‘balanced’