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DOC Update warm start example in ensemble user guide #28998

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May 13, 2024
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DOC Update warm start example in ensemble user guide #28998

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complete warm start example
lucyleeow May 11, 2024
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lucyleeow May 12, 2024
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17 changes: 16 additions & 1 deletion doc/modules/ensemble.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -603,7 +603,22 @@ fitted model.

::

>>> _ = est.set_params(n_estimators=200, warm_start=True) # set warm_start and new nr of trees
>>> import numpy as np
>>> from sklearn.metrics import mean_squared_error
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import GradientBoostingRegressor

>>> X, y = make_friedman1(n_samples=1200, random_state=0, noise=1.0)
>>> X_train, X_test = X[:200], X[200:]
>>> y_train, y_test = y[:200], y[200:]
>>> est = GradientBoostingRegressor(
... n_estimators=100, learning_rate=0.1, max_depth=1, random_state=0,
... loss='squared_error'
... )
>>> est = est.fit(X_train, y_train) # fit with 100 trees
>>> mean_squared_error(y_test, est.predict(X_test))
5.00...
>>> _ = est.set_params(n_estimators=200, warm_start=True) # set warm_start and increase num of trees
>>> _ = est.fit(X_train, y_train) # fit additional 100 trees to est
>>> mean_squared_error(y_test, est.predict(X_test))
3.84...
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