本文结构：

1. 什么是 LightGBM
2. 怎么调参
3. 和 xgboost 的代码比较

1. 什么是 LightGBM

Light GBM is a gradient boosting framework that uses tree based learning algorithm.

LightGBM 垂直地生长树，即 leaf-wise，它会选择最大 delta loss 的叶子来增长。

而以往其它基于树的算法是水平地生长，即 level-wise，

当生长相同的叶子时，Leaf-wise 比 level-wise 减少更多的损失。

高速，高效处理大数据，运行时需要更低的内存，支持 GPU

不要在少量数据上使用，会过拟合，建议 10,000+ 行记录时使用。

2. 怎么调参

下面几张表为重要参数的含义和如何应用

调参

下表对应了 Faster Speed ，better accuracy ，over-fitting 三种目的时，可以调的参数

3. lightGBM 和 xgboost 的代码比较

#xgboost
dtrain = xgb.DMatrix(x_train,label=y_train)
dtest = xgb.DMatrix(x_test)


# lightgbm
train_data = lgb.Dataset(x_train,label=y_train)

setting parameters:

#xgboost
parameters = {
    'max_depth':7, 
    'eta':1, 
    'silent':1,
    'objective':'binary:logistic',
    'eval_metric':'auc',
    'learning_rate':.05}

# lightgbm
param = {
    'num_leaves':150, 
    'objective':'binary',
    'max_depth':7,
    'learning_rate':.05,
    'max_bin':200}
param['metric'] = ['auc', 'binary_logloss']

training model :

#xgboost
num_round = 50
from datetime import datetime 
start = datetime.now() 
xg = xgb.train(parameters,dtrain,num_round) 
stop = datetime.now()

# lightgbm
num_round = 50
start = datetime.now()
lgbm = lgb.train(param,train_data,num_round)
stop = datetime.now()

Execution time of the model:

#xgboost
execution_time_xgb = stop - start 
execution_time_xgb

# lightgbm
execution_time_lgbm = stop - start
execution_time_lgbm

predicting model on test set:

#xgboost
ypred = xg.predict(dtest) 
ypred

# lightgbm
ypred2 = lgbm.predict(x_test)
ypred2[0:5]   

Converting probabilities into 1 or 0:

#xgboost
for i in range(0,9769): 
    if ypred[i] >= .5:       # setting threshold to .5 
       ypred[i] = 1 
    else: 
       ypred[i] = 0

# lightgbm
for i in range(0,9769):
    if ypred2[i] >= .5:       # setting threshold to .5
       ypred2[i] = 1
    else:  
       ypred2[i] = 0

calculating accuracy of our model :

#xgboost
from sklearn.metrics import accuracy_score 
accuracy_xgb = accuracy_score(y_test,ypred) 
accuracy_xgb

# lightgbm
accuracy_lgbm = accuracy_score(ypred2,y_test)
accuracy_lgbm
y_test.value_counts()
from sklearn.metrics import roc_auc_score

calculating roc_auc_score:

#xgboost
auc_xgb =  roc_auc_score(y_test,ypred)

# lightgbm
auc_lgbm = roc_auc_score(y_test,ypred2)

最后可以建立一个 dataframe 来比较 Lightgbm 和 xgb:

auc_lgbm comparison_dict = {
    'accuracy score':(accuracy_lgbm,accuracy_xgb),
    'auc score':(auc_lgbm,auc_xgb),
    'execution time':(execution_time_lgbm,execution_time_xgb)}

comparison_df = DataFrame(comparison_dict) 
comparison_df.index= ['LightGBM','xgboost'] 
comparison_df

学习资料：
https://medium.com/@pushkarmandot/https-medium-com-pushkarmandot-what-is-lightgbm-how-to-implement-it-how-to-fine-tune-the-parameters-60347819b7fc
https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/

推荐阅读 历史技术博文链接汇总
http://www.jianshu.com/p/28f02bb59fe5
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