读取数据
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
#reduce_mem_usage 函数通过调整数据类型，减少数据在内存中占用的空间
def reduce_mem_usage(df):
""" iterate through all the columns of a dataframe and modify the data type
to 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].dtype
if 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 62099672.00 MB
Memory usage after optimization is: 16520303.00 MB
Decreased by 73.4%
continuous_feature_names = [x for x in sample_feature.columns if x not in ['price','brand','model','brand']]
线性回归 & 五折交叉验证 & 模拟真实情况
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']
简单建模
from sklearn.linear_model import LinearRegression
model = LinearRegression(normalize=True)
model = model.fit(train_X, train_y)
查看训练的线性回归模型的截距（intercept）与权重(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)
绘制特征v_9的值与标签的散点图，图片发现模型的预测结果（蓝色点）与真实标签（黑色点）的分布差异较大，且部分预测值出现了小于0的情况，说明模型存在一些问题
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
通过作图发现数据的标签（price）呈现长尾分布，不利于建模预测。原因是很多模型都假设数据误差项符合正态分布，而长尾分布的数据违背了这一假设。
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>
对标签进行 l o g ( x + 1 ) log(x+1) log(x+1) 变换，使标签贴近于正态分布
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)]
再次进行可视化，发现预测结果与真实值较为接近，且未出现异常状况
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 五折交叉验证
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 result
return 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
使用线性回归模型，对未处理标签的特征数据进行五折交叉验证（Error 1.36）
print('AVG:', np.mean(scores))
AVG: 1.3658024042408414
使用线性回归模型，对处理过标签的特征数据进行五折交叉验证（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: 0.19325301535176911
scores = pd.DataFrame(scores.reshape(1,-1))
scores.columns = ['cv' + str(x) for x in range(1, 6)]
scores.index = ['MAE']
scores
| cv1 | cv2 | cv3 | cv4 | cv5 | |
|---|---|---|---|---|---|
| MAE | 0.191642 | 0.194986 | 0.192737 | 0.195329 | 0.19445 |
模拟真实情况
# 采用时间顺序对数据集进行分隔。
# 选用靠前时间的4/5样本当作训练集，靠后时间的1/5当作验证集，
# 最终结果与五折交叉验证差距不
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))
0.1957766704050743
绘制学习率曲线与验证曲线
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()#区域
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'>
多种模型对比
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)
线性模型 & 嵌入式特征选择
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] = scores
print(model_name + ' is finished')
LinearRegression is finished
Ridge is finished
Lasso is finished
对三种方法的效果对比
result = pd.DataFrame(result)
result.index = ['cv' + str(x) for x in range(1, 6)]
result
| LinearRegression | Ridge | Lasso | |
|---|---|---|---|
| cv1 | 0.191642 | 0.195665 | 0.382708 |
| cv2 | 0.194986 | 0.198841 | 0.383916 |
| cv3 | 0.192737 | 0.196629 | 0.380754 |
| cv4 | 0.195329 | 0.199255 | 0.385683 |
| cv5 | 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正则化在拟合过程中通常都倾向于让权值尽可能小，最后构造一个所有参数都比较小的模型。因为一般认为参数值小的模型比较简单，能适应不同的数据集，也在一定程度上避免了过拟合现象。可以设想一下对于一个线性回归方程，若参数很大，那么只要数据偏移一点点，就会对结果造成很大的影响；但如果参数足够小，数据偏移得多一点也不会对结果造成什么影响，专业一点的说法是『抗扰动能力强』
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正则化有助于生成一个稀疏权值矩阵，进而可以用于特征选择。如下图，我们发现power与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，决策树通过信息熵或GINI指数选择分裂节点时，优先选择的分裂特征也更加重要，这同样是一种特征选择的方法。XGBoost与LightGBM模型中的model_importance指标正是基于此计算的
非线性模型
除了线性模型以外，还有许多我们常用的非线性模型如下，在此篇幅有限不再一一讲解原理。我们选择了部分常用模型与线性模型进行效果比对。
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] = scores
print(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
| LinearRegression | DecisionTreeRegressor | RandomForestRegressor | GradientBoostingRegressor | MLPRegressor | XGBRegressor | LGBMRegressor | |
|---|---|---|---|---|---|---|---|
| cv1 | 0.191642 | 0.184566 | 0.136266 | 0.168626 | 124.299426 | 0.168698 | 0.141159 |
| cv2 | 0.194986 | 0.187029 | 0.139693 | 0.171905 | 257.886236 | 0.172258 | 0.143363 |
| cv3 | 0.192737 | 0.184839 | 0.136871 | 0.169553 | 236.829589 | 0.168604 | 0.142137 |
| cv4 | 0.195329 | 0.182605 | 0.138689 | 0.172299 | 130.197264 | 0.172474 | 0.143461 |
| cv5 | 0.194450 | 0.186626 | 0.137420 | 0.171206 | 268.090236 | 0.170898 | 0.141921 |
可以看到随机森林模型在每一个fold中均取得了更好的效果
模型调参
在此我们介绍了三种常用的调参方法如下：
- 贪心算法 https://www.jianshu.com/p/ab89df9759c8
- 网格调参 https://blog.csdn.net/weixin_43172660/article/details/83032029
- 贝叶斯调参 https://blog.csdn.net/linxid/article/details/81189154
## LGB的参数集合：
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 = []
贪心调参
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] = score
best_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] = score
best_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>
Grid Search 调参
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
贝叶斯调参
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