Advanced LightGBM Example 这一部分是一个关于LightGBM进一步使用的例子(还是使用官网的例子进行说明)。主要介绍以下的内容。 初始设置样本权重 模型的三种保存方式(1. 保存为txt格式, 2. 保存为json格式, 3. 使用pickle保存) 模型的继续训练(将保存的模型重新导入, 继续训练) 训练过程中逐渐减小learning rate 训练...
2. 安装cmake:sudo apt-get install cmake 3. 安装lightgbm:根据文档安装 git clone --recursive https://github.com/microsoft/LightGBM 4. 编译lightgbm cd LightGBM mkdir build cd build cmake .. make -j4 编译过程中出现importerror "No Module named Setuptools"错误,需要把Setuptools安装一下 sudo apt-...
Im trying to simulate a Typewriter effect with javascript. Theorically it should work with my code: That should be it, when i call TypeWrite("example", "p_test"); it should write e... SwiftUI Schedule Countdown Timer - Pause & Start ...
git clone --recursive https://github.com/microsoft/LightGBM ; cd LightGBM mkdir build ; cd build cmake .. # 开启MPI通信机制,训练更快 # cmake -DUSE_MPI=ON .. # GPU版本,训练更快 # cmake -DUSE_GPU=1 .. make -j4 安装方法:pip安装 # 默认版本 pip install lightgbm # MPI版本 pip i...
For example, when the max_depth=7 the depth-wise tree can get good accuracy, but setting num_leaves to 127 may cause over-fitting, and setting it to 70 or 80 may get better accuracy than depth-wise. 4、正则化参数lambda_l1(reg_alpha), lambda_l2(reg_lambda),毫无疑问,是降低过拟合的...
(preds:array,train_data:Dataset)->name:string,eval_result:float,is_higher_better:bool # binary error #NOTE:when youdocustomized lossfunction,thedefaultprediction value is margin # This may make built-inevalution metric calculate wrong results # For example,we are doing log likelihood loss,the ...
plt.title('Receiver Operating Characteristic example') plt.legend(loc="lower right") plt.show() # 计算混淆矩阵 cm = confusion_matrix(y_test, y_pred) # 打印模型拟合效果的评价指标 print(f"ROC AUC: {roc_auc}") print(f"Confusion Matrix:\n{cm}") ...
The generic OpenCL ICD packages (for example, Debian packagecl-icd-libopencl1andcl-icd-opencl-dev) can also be used. libboost 1.56 或更新版本 (1.61 或最新推荐的版本). We use Boost.Compute as the interface to GPU, which is part of the Boost library since version 1.61. However, since ...
上图左边为基于 one-hot 编码进行分裂,后图为 LightGBM 基于 many-vs-many 进行分裂,右边叶子节点的含义是X = A或者X = C 放到左孩子,其余放到右孩子,右边的切分方法,数据会被切分到两个比较大的空间,进一步的学习也会更好。 这里有一个机器学习常识问题:为什么连续特征可以直接构建直方图,但类别特征要按照GH...
We solve a not classical survival problem where we have not to estimate the probability of surviving past time, but we estimate the probability that a specific event occurs at the end of the day. This is a simple example but it shows how to applicate survival modeling techniques, with classi...