据开发者所说超越Lightgbm和XGBoost的又一个神器，不过具体性能，还要看在比赛中的表现了。
整理一下里面简单的教程和参数介绍，很多参数不是那种重要，只解释部分重要的参数，训练时需要重点考虑的。
Quick start
CatBoostClassifier
import numpy as np
import catboost as cb
train_data = np.random.randint(0, 100, size=(100, 10))
train_label = np.random.randint(0, 2, size=(100))
test_data = np.random.randint(0,100, size=(50,10))
model = cb.CatBoostClassifier(iterations=2, depth=2, learning_rate=0.5, loss_function='Logloss',
logging_level='Verbose')
model.fit(train_data, train_label, cat_features=[0,2,5])
preds_class = model.predict(test_data)
preds_probs = model.predict_proba(test_data)
print('class = ',preds_class)
print('proba = ',preds_probs)参数
CatBoostClassifier/CatBoostRegressor
通用参数
learning_rate(eta)=automatically
depth(max_depth)=6: 树的深度
l2_leaf_reg(reg_lambda)=3 L2正则化系数
- n_estimators(num_boost_round)(num_trees=1000)=1000: 解决ml问题的树的最大数量
- one_hot_max_size=2: 对于某些变量进行one-hot编码
- loss_function=’Logloss’:
RMSE
Logloss
MAE
CrossEntropy- custom_metric=None
RMSE
Logloss
MAE
CrossEntropy
Recall
Precision
F1
Accuracy
AUC
R2- eval_metric=Optimized objective
RMSE
Logloss
MAE
CrossEntropy
Recall
Precision
F1
Accuracy
AUC
R2- nan_mode=None：处理NAN的方法
Forbidden
Min
Max- leaf_estimation_method=None：迭代求解的方法，梯度和牛顿
Newton
Gradient- random_seed=None: 训练时候的随机种子
性能参数
- thread_count=-1：训练时所用的cpu/gpu核数
- used_ram_limit=None：CTR问题，计算时的内存限制
- gpu_ram_part=None：GPU内存限制
处理单元设置
- task_type=CPU：训练的器件
devices=None：训练的GPU设备ID
counter_calc_method=None,
- leaf_estimation_iterations=None,
- use_best_model=None,
- verbose=None,
- model_size_reg=None,
- rsm=None,
- logging_level=None,
- metric_period=None,
- ctr_leaf_count_limit=None,
- store_all_simple_ctr=None,
- max_ctr_complexity=None,
- has_time=None,
- classes_count=None,
class_weights=None,
random_strength=None,
- name=None,
- ignored_features=None,
- train_dir=None,
custom_loss=None,
bagging_temperature=None
border_count=None
feature_border_type=None,
- save_snapshot=None,
- snapshot_file=None,
fold_len_multiplier=None,
allow_writing_files=None,
- final_ctr_computation_mode=None,
- approx_on_full_history=None,
- boosting_type=None,
- simple_ctr=None,
- combinations_ctr=None,
per_feature_ctr=None,
device_config=None,
bootstrap_type=None,
subsample=None,
colsample_bylevel=None,
- random_state=None,
objective=None,
max_bin=None,
- scale_pos_weight=None,
- gpu_cat_features_storage=None,
- data_partition=None
CatBoostClassifier
属性(attribute)：
- is_fitted_
- tree_count_
- feature_importances_
- random_seed_
方法(method):
fit
X: 输入数据数据类型可以是，list; pandas.DataFrame; pandas.Series
y=None
- cat_features=None: 拿来做处理的类别特征
- sample_weight=None: 输入数据的样本权重
- logging_level=None: 控制是否输出日志信息，或者何种信息
- plot=False: 训练过程中，绘制，度量值，所用时间等
- eval_set=None: 验证集合，数据类型list(X, y)tuples
- baseline=None
- use_best_model=None
- verbose=None
predict
返回验证样本所属类别，数据类型为np.array
predict_proba
返回验证样本所属类别的概率，数据类型为np.array
get_feature_importance
eval_metrics
save_model
load_model
get_params
score
教程(tutorial)
