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Calculate base_score based on input labels for mae. (#8107)

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Fit an intercept as base score for abs loss.
This commit is contained in:
Jiaming Yuan
2022-09-20 20:53:54 +08:00
committed by GitHub
parent 4f42aa5f12
commit fffb1fca52
42 changed files with 999 additions and 343 deletions
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10
tests/cpp/linear/test_linear.cc
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@@ -18,10 +18,7 @@ TEST(Linear, Shotgun) {
auto p_fmat = xgboost::RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
auto lparam = xgboost::CreateEmptyGenericParam(GPUIDX);
LearnerModelParam mparam;
mparam.num_feature = kCols;
mparam.num_output_group = 1;
mparam.base_score = 0.5;
LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
{
auto updater = std::unique_ptr<xgboost::LinearUpdater>(
@@ -54,10 +51,7 @@ TEST(Linear, coordinate) {
auto p_fmat = xgboost::RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
auto lparam = xgboost::CreateEmptyGenericParam(GPUIDX);
LearnerModelParam mparam;
mparam.num_feature = kCols;
mparam.num_output_group = 1;
mparam.base_score = 0.5;
LearnerModelParam mparam{MakeMP(kCols, .5, 1)};
auto updater = std::unique_ptr<xgboost::LinearUpdater>(
xgboost::LinearUpdater::Create("coord_descent", &lparam));