ML之catboost：基于自定义数据集利用catboost 算法实现回归预测(训练采用CPU和GPU两种方式)
目录
基于自定义数据集利用catboost 算法实现回归预测(训练采用CPU和GPU两种方式)
输出结果
# T1、训练采用CPU
# T2、训练采用GPU
实现代码
基于自定义数据集利用catboost 算法实现回归预测(训练采用CPU和GPU两种方式) 输出结果 # T1、训练采用CPU0: learn: 7.9608417 total: 50.2ms remaining: 1.46s
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[14.67884178 20. ]# ML之catboost：基于自定义数据集实现回归预测
from catboost import CatBoostRegressor
#1、定义数据集
train_data = [[12, 14, 16, 18],
[23, 25, 27, 29],
[32, 34, 36, 38]]
train_labels = [10, 20, 30]
eval_data = [[2, 4, 6, 8],
[20, 21, 24, 33]]
#2、模型预测
model_CatBR = CatBoostRegressor(iterations=30, learning_rate=0.1, depth=2)
model_CatBR.fit(train_data, train_labels)
preds = model_CatBR.predict(eval_data)
print(preds)
