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products:ict:python:machine_learning:light_gbm [2023/10/12 17:29] – wikiadminproducts:ict:python:machine_learning:light_gbm [2023/10/12 17:30] (current) – wikiadmin
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 4. **Creating a Dataset**: 4. **Creating a Dataset**:
  
-   To efficiently use LightGBM, you need to create a `Dataset` object from your data. This object is optimized for training and prediction.+To efficiently use LightGBM, you need to create a `Dataset` object from your data. This object is optimized for training and prediction.
  
  
-   train_data = lgb.Dataset(data=X_train, label=y_train) +train_data = lgb.Dataset(data=X_train, label=y_train) 
-   test_data = lgb.Dataset(data=X_test, label=y_test, reference=train_data)+test_data = lgb.Dataset(data=X_test, label=y_test, reference=train_data)
  
  
 5. **Setting Parameters**: 5. **Setting Parameters**:
  
-   LightGBM has a wide range of parameters that control the training process and the model's behavior. Some important parameters include:+LightGBM has a wide range of parameters that control the training process and the model's behavior. Some important parameters include:
  
-   - `objective`: Specifies the learning task (e.g., "regression," "binary," or "multiclass"). +- `objective`: Specifies the learning task (e.g., "regression," "binary," or "multiclass").
-   - `num_leaves`: Number of leaves in each tree. It controls the complexity of the model. +
-   - `learning_rate`: Step size for updates during training. +
-   - `max_depth`: Maximum depth of the tree. +
-   - `num_boost_round`: Number of boosting iterations (trees). +
-   - `metric`: Evaluation metric for model performance.+
  
-   You can set these parameters in a dictionary and pass it to the training process.+- `num_leaves`: Number of leaves in each tree. It controls the complexity of the model. 
 + 
 +- `learning_rate`: Step size for updates during training. 
 + 
 +- `max_depth`: Maximum depth of the tree. 
 + 
 +- `num_boost_round`: Number of boosting iterations (trees). 
 + 
 +- `metric`: Evaluation metric for model performance. 
 + 
 +You can set these parameters in a dictionary and pass it to the training process.
  
 6. **Training the Model**: 6. **Training the Model**:
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-   The `early_stopping_rounds` parameter allows the training to stop early if the evaluation metric doesn't improve for a specified number of rounds on the validation set.+The `early_stopping_rounds` parameter allows the training to stop early if the evaluation metric doesn't improve for a specified number of rounds on the validation set.
  
 7. **Making Predictions**: 7. **Making Predictions**:
products/ict/python/machine_learning/light_gbm.1697113767.txt.gz · Last modified: 2023/10/12 17:29 by wikiadmin