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add params for avoiding warn and improving perf.
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@@ -1503,7 +1503,7 @@
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"forest_reg = RandomForestRegressor(random_state=42)\n",
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"# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
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"grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
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" scoring='neg_mean_squared_error')\n",
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" scoring='neg_mean_squared_error', return_train_score=True)\n",
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"grid_search.fit(housing_prepared, housing_labels)"
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]
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},
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