随机森林中的决策树可视化图

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import pdpbox

In [2]:

pdpbox.__version__

Out[2]:

'0.2.1'

In [3]:

import matplotlib

In [4]:

matplotlib.__version__

Out[4]:

'3.1.1'

In [5]:

import numpy as np
import pandas as pd
from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.kernel_ridge import KernelRidge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import KFold, cross_val_score
from sklearn.metrics import mean_squared_error
from mlxtend.regressor import StackingCVRegressor
import xgboost as xgb
import lightgbm as lgb
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import zipfile
from sklearn.model_selection import KFold
import lightgbm as lgb
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor

In [6]:

df=pd.read_excel('训练验证集.xlsx')

In [7]:

y_train=df.iloc[:,-2:-1]
x_train=df.iloc[:,:-2]

In [8]:

# 转换成矩阵
x_train = np.array(x_train)
y_train = np.array(y_train)

In [9]:

rf= RandomForestRegressor(n_estimators=151,
            min_samples_split=6,
            max_features=0.32757967156069284, # float
            max_depth=15,
            random_state=2
        )

In [10]:

rf.fit(x_train,y_train[:,0])

Out[10]:

RandomForestRegressor(max_depth=15, max_features=0.32757967156069284,
                      min_samples_split=6, n_estimators=151, random_state=2)

In [11]:

len(rf.estimators_)

Out[11]:

151

In [12]:

estimator=rf.estimators_[7]
estimator

Out[12]:

DecisionTreeRegressor(max_depth=15, max_features=0.32757967156069284,
                      min_samples_split=6, random_state=2081981515)

In [42]:

from sklearn import tree
tree.plot_tree(estimator)