python二手车价格预测_数据挖掘实战--二手车交易价格预测（四）建模调参

建模调参的过程中，尝试了两种不同的调参方式——贪心调参和贝叶斯调参。

最近事情太多，就划个水给自己记录一下即可。

贪心调参

把求解的问题分成若干个子问题；对每个子问题求解，得到子问题的局部最优解；把子问题的解局部最优解合成原来问题的一个解。

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 = []

# 1_turning_obj

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

# 2_turning_leaves

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

# 3_turning_depth

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())])

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)

}

)

原文链接:https://blog.csdn.net/nonejia/article/details/105255231