title: What is leaf_values from Python LightGBM?
body: <p>I'm using the LightGBM Package. </p>
  
  <p>I have successfully created a new tree using "create_tree_digraph" but I face some trouble understanding the result. </p>
  
  <p>There is "leaf_value" in a leaf node. I don't know what it means. Please, somebody help me understand this. Thanks. :)</p>
  
  <p>I used this example code from here: <a href="https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/" rel="nofollow noreferrer">https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/</a></p>
  
  <pre><code>#importing standard libraries 
  import numpy as np 
  import pandas as pd 
  from pandas import Series, DataFrame 
  import graphviz
  
  import lightgbm as lgb 
  
  #loading our training dataset 'adult.csv' with name 'data' using pandas 
  data=pd.read_csv('./adult.csv',header=None) 
  
  #Assigning names to the columns 
  data.columns=['age','workclass','fnlwgt','education','education-num','marital_Status','occupation','relationship','race','sex','capital_gain','capital_loss','hours_per_week','native_country','Income'] 
  
  # Label Encoding our target variable 
  from sklearn.preprocessing import LabelEncoder,OneHotEncoder
  l=LabelEncoder()
  l.fit(data.Income) 
  
  data.Income=Series(l.transform(data.Income))  #label encoding our target variable  
  
  #One Hot Encoding of the Categorical features 
  one_hot_workclass=pd.get_dummies(data.workclass) 
  one_hot_education=pd.get_dummies(data.education) 
  
  #removing categorical features 
  data.drop(['workclass','education','marital_Status','occupation','relationship','race','sex','native_country'],axis=1,inplace=True)  
  
  #Merging one hot encoded features with our dataset 'data' 
  data=pd.concat([data,one_hot_workclass,one_hot_education],axis=1) 
  
  #Here our target variable is 'Income' with values as 1 or 0.  
  #Separating our data into features dataset x and our target dataset y 
  x=data.drop('Income',axis=1) 
  y=data.Income 
  
  #Imputing missing values in our target variable 
  y.fillna(y.mode()[0],inplace=True) 
  
  #Now splitting our dataset into test and train 
  from sklearn.model_selection import train_test_split 
  x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.3)
  
  train_data=lgb.Dataset(x_train,label=y_train)
  
  #setting parameters for lightgbm
  param = {'num_leaves':150, 'objective':'binary','max_depth':3,'learning_rate':.05,'max_bin':200}
  param['metric'] = ['auc', 'binary_logloss']
  
  #training our model using light gbm
  num_round=50
  lgbm=lgb.train(param,train_data,num_round)
  
  graph = lgb.create_tree_digraph(lgbm)
  graph.render(view=True)
  </code></pre>
  
  <p>Then I applied 'create_tree_digraph' function.</p>
  
  <p><a href="https://i.stack.imgur.com/jCT1d.png" rel="nofollow noreferrer">Pics</a></p>