[ML] Add a constant to the prediction which minimises the unregularised loss for classification and regression

docs/CHANGELOG.asciidoc

@@ -65,6 +65,9 @@
 * Improve false positive rates from periodicity test for time series anomaly detection.
   (See {ml-pull}1177[#1177].)
 * Break progress reporting of data frame analyses into multiple phases. (See {ml-pull}1179[#1179].)
+* Really centre the data before training for classification and regression begins. This
+  means we can choose more optimal smoothing bias and should reduce the number of trees.
+  (See {ml-pull}1192[#1192].)
 
 === Bug Fixes
 

include/maths/CBoostedTreeImpl.h

@@ -247,6 +247,7 @@ class MATHS_EXPORT CBoostedTreeImpl final {
                                               const core::CPackedBitVector& trainingRowMask,
                                               const core::CPackedBitVector& testingRowMask,
                                               double eta,
+                                              double lambda,
                                               TNodeVec& tree) const;
 
     //! Compute the mean of the loss function on the masked rows of \p frame.

lib/api/unittest/CDataFrameAnalyzerFeatureImportanceTest.cc

@@ -485,11 +485,11 @@ BOOST_FIXTURE_TEST_CASE(testRegressionFeatureImportanceNoImportance, SFixture) {
             double c1{readShapValue(result, "c1")};
             double prediction{
                 result["row_results"]["results"]["ml"]["target_prediction"].GetDouble()};
-            // c1 explains 94% of the prediction value, i.e. the difference from the prediction is less than 2%.
+            // c1 explains 94% of the prediction value, i.e. the difference from the prediction is less than 6%.
             BOOST_REQUIRE_CLOSE(c1, prediction, 6.0);
             for (const auto& feature : {"c2", "c3", "c4"}) {
                 double c = readShapValue(result, feature);
-                BOOST_REQUIRE_SMALL(c, 2.0);
+                BOOST_REQUIRE_SMALL(c, 3.0);
                 cNoImportanceMean.add(std::fabs(c));
             }
         }

lib/maths/CBoostedTreeImpl.cc

@@ -523,8 +523,8 @@ CBoostedTreeImpl::TNodeVec CBoostedTreeImpl::initializePredictionsAndLossDerivat
 
     // At the start we will centre the data w.r.t. the given loss function.
     TNodeVec tree{CBoostedTreeNode{m_Loss->numberParameters()}};
-    this->refreshPredictionsAndLossDerivatives(frame, trainingRowMask,
-                                               testingRowMask, 1.0, tree);
+    this->refreshPredictionsAndLossDerivatives(frame, trainingRowMask, testingRowMask,
+                                               1.0 /*eta*/, 0.0 /*lambda*/, tree);
 
     return tree;
 }
@@ -585,8 +585,9 @@ CBoostedTreeImpl::trainForest(core::CDataFrame& frame,
 
         if (tree.size() > 1) {
             scopeMemoryUsage.add(tree);
-            this->refreshPredictionsAndLossDerivatives(frame, trainingRowMask,
-                                                       testingRowMask, eta, tree);
+            this->refreshPredictionsAndLossDerivatives(
+                frame, trainingRowMask, testingRowMask, eta,
+                m_Regularization.leafWeightPenaltyMultiplier(), tree);
             forest.push_back(std::move(tree));
             eta = std::min(1.0, m_EtaGrowthRatePerTree * eta);
             retries = 0;
@@ -990,13 +991,12 @@ void CBoostedTreeImpl::refreshPredictionsAndLossDerivatives(core::CDataFrame& fr
                                                             const core::CPackedBitVector& trainingRowMask,
                                                             const core::CPackedBitVector& testingRowMask,
                                                             double eta,
+                                                            double lambda,
                                                             TNodeVec& tree) const {
 
     using TArgMinLossVec = std::vector<CArgMinLoss>;
 
-    TArgMinLossVec leafValues(
-        tree.size(),
-        m_Loss->minimizer(m_Regularization.leafWeightPenaltyMultiplier(), m_Rng));
+    TArgMinLossVec leafValues(tree.size(), m_Loss->minimizer(lambda, m_Rng));
     auto nextPass = [&] {
         bool done{true};
         for (const auto& value : leafValues) {