本文比较了LGBMClassifier的不同参数设置对模型性能的影响，包括正则化系数、叶子数、深度等，展示了不同配置下准确率的变化。
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clf = LGBMClassifier(reg_alpha=0.15,reg_lambda=10,num_leaves=60,max_depth=7,n_estimators=1500,
learning_rate=0.01,bagging_fraction=0.7,bagging_freq=10,max_bin=150,
min_data_in_leaf=30,feature_fraction=0.7,random_state=1)
lgm [[228612 373] [ 41600 2732]] [[97835 202] [18095 1004]]
clf = LGBMClassifier(reg_alpha=0.5,reg_lambda=10,num_leaves=60,max_depth=7,n_estimators=1500,
learning_rate=0.01,bagging_fraction=0.7,bagging_freq=10,max_bin=150,
min_data_in_leaf=30,feature_fraction=0.7,random_state=1)
lgm [[228605 380] [ 41581 2751]] [[97832 205] [18076 1023]]
clf = LGBMClassifier(reg_alpha=0.5,reg_lambda=10,num_leaves=60,max_depth=7,n_estimators=1500,
learning_rate=0.01,bagging_fraction=0.7,bagging_freq=10,max_bin=100,
min_data_in_leaf=30,feature_fraction=0.7,random_state=1)
[[228618 367] [ 41581 2751]] [[97838 199] [18084 1015]]
clf = LGBMClassifier(reg_alpha=0.5,reg_lambda=10,num_leaves=60,max_depth=7,n_estimators=1500,
learning_rate=0.01,bagging_fraction=0.6,bagging_freq=10,max_bin=100,
min_data_in_leaf=30,feature_fraction=0.7,random_state=1)
lgm [[228623 362] [ 41643 2689]] [[97826 211] [18094 1005]]
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