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--- products:ict:python:machine_learning:light_gbm [2023/10/12 17:29] – wikiadmin
+++ products:ict:python:machine_learning:light_gbm [2023/10/12 17:30] (current) – wikiadmin
@@ Line 35: / Line 35: @@
 . **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.
 . **Training the Model**:
@@ Line 55: / Line 60: @@
-   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.
 . **Making Predictions**: