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doc/source/libraries/xorbits_train/lightgbm.rst

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+.. _10min_lightgbm:
+
+===================================
+10 minutes to :code:`xorbits.lightgbm`
+===================================
+
+.. currentmodule:: xorbits.lightgbm
+
+This is a short introduction to :code:`xorbits.lightgbm` which is originated from LightGBM's quickstart.
+
+Let's take :code:`LGBMRegressor` as an example and explain how to build a regression model, find the relationship between independent variables (features) and the dependent variable (target), and make predictions based on these relationships.
+
+Customarily, we import and init as follows:
+
+.. ipython:: python
+
+   import xorbits
+   import xorbits.numpy as np
+   from xorbits.lightgbm import LGBMRegressor
+   from xorbits.sklearn.model_selection import train_test_split
+   from xorbits.sklearn.metrics import mean_squared_error
+   xorbits.init()
+
+Model Creation
+---------------
+First, we build a :code:`LGBMRegressor` model and define its parameters.
+
+This model has many adjustable hyperparameters that allow you to configure parameters such as tree depth, the number of leaf nodes, learning rate, and more to optimize the model's performance.
+
+..  ipython:: python
+
+    lgbm_regressor = LGBMRegressor(learning_rate=0.05,n_estimators=100)
+
+:code:`.get_params` method returns a dictionary containing all the parameter names of the model along with their corresponding values. You can inspect these values to understand the current configuration of the model.
+
+Inspect the parameters of the LightGBM regressor.
+
+..  ipython:: python
+
+    paras=lgbm_regressor.get_params()
+    paras
+
+Set/modify parameters.
+
+:code:`.set_params` method allows you to dynamically modify the parameter settings of a machine learning model by specifying parameter names and their corresponding values, without the need to recreate the model object.
+
+..  ipython:: python
+
+    lgbm_regressor.set_params(learning_rate=0.1, n_estimators=100)
+    lgbm_regressor.get_params()
+
+Data Preparation
+---------------
+We can use real data as input. For the sake of simplicity, we will use randomly generated x and y data as an example.
+
+..  ipython:: python
+
+    x = np.random.rand(100)
+    y_regression = 2 * x + 1 + 0.1 * np.random.randn(100)
+    x=x.reshape(-1, 1)
+
+In order to train the model, we split the dataset into a training set and a test set.
+
+..  ipython:: python
+
+    X_train, X_test, y_train, y_test = train_test_split(x, y_regression, test_size=0.2)
+
+Model Training
+---------------
+The :code:`.fit` method takes the training data (independent variable x and dependent variable y) and fits the model to the data.
+
+The model adjusts its parameters to minimize the error between the predicted values and the actual observations.
+
+..  ipython:: python
+
+    lgbm_regressor.fit(X_train, y_train)
+
+Model Prediction
+---------------
+
+Once you have trained a model, you can use the :code:`.predict` method to apply that model to new data and generate predictions for the new data.
+
+..  ipython:: python
+
+    y_pred = lgbm_regressor.predict(X_test)
+    y_pred
+
+Model Evaluation
+---------------
+
+:code:`.score` is typically used to assess the performance of a machine learning model.
+
+In regression problems, the :code:`.score` method usually returns the coefficient of determination (R-squared) score, which represents the model's ability to explain the variability in the dependent variable.
+
+Calculate the model's estimated accuracy on the test set.
+
+..  ipython:: python
+
+    mse = mean_squared_error(y_test, y_pred)
+    mse
+
+    accuracy = lgbm_regressor.score(X_test, y_test)
+    accuracy