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[doc] Fix typo. [skip ci] (#9904)

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Jiaming Yuan 2023-12-20 09:17:00 +08:00 committed by GitHub
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doc/tutorials/intercept.rst
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@ -38,8 +38,8 @@ and multi-class, the ``base_margin`` is a matrix with size ``(n_samples, n_targe
reg_1 = xgb.XGBRegressor() reg_1 = xgb.XGBRegressor()
# Feed the prediction into the next model # Feed the prediction into the next model
reg.fit(X, y, base_margin=m) reg_1.fit(X, y, base_margin=m)
reg.predict(X, base_margin=m) reg_1.predict(X, base_margin=m)
It specifies the bias for each sample and can be used for stacking an XGBoost model on top It specifies the bias for each sample and can be used for stacking an XGBoost model on top
@ -79,7 +79,8 @@ function, hence:
E[y_i] = \exp{(F(x_i) + b_i)} E[y_i] = \exp{(F(x_i) + b_i)}
As a result, if you are feeding outputs from models like GLM with a corresponding As a result, if you are feeding outputs from models like GLM with a corresponding
objective function, make sure the outputs are not yet transformed by the inverse link. objective function, make sure the outputs are not yet transformed by the inverse link
(activation).
In the case of ``base_score`` (intercept), it can be accessed through In the case of ``base_score`` (intercept), it can be accessed through
:py:meth:`~xgboost.Booster.save_config` after estimation. Unlike the ``base_margin``, the :py:meth:`~xgboost.Booster.save_config` after estimation. Unlike the ``base_margin``, the
@ -91,13 +92,13 @@ and the logit link function as an example, given the ``base_score`` as 0.5,
E[y_i] = g^{-1}{(F(x_i) + g(intercept))} E[y_i] = g^{-1}{(F(x_i) + g(intercept))}
and 0.5 is the same as :math:`base_score = g^{-1}(0) = 0.5`. This is more intuitive if you and 0.5 is the same as :math:`base\_score = g^{-1}(0) = 0.5`. This is more intuitive if
remove the model and consider only the intercept, which is estimated before the model is you remove the model and consider only the intercept, which is estimated before the model
fitted: is fitted:
.. math:: .. math::
E[y] = g^{-1}{g(intercept))} \\ E[y] = g^{-1}{(g(intercept))} \\
E[y] = intercept E[y] = intercept
For some objectives like MAE, there are close solutions, while for others it's estimated For some objectives like MAE, there are close solutions, while for others it's estimated