Task4 建模调参

本文主要是介绍Task4 建模调参，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

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

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

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

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

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