diff --git a/include/api/CDataFrameAnalysisRunner.h b/include/api/CDataFrameAnalysisRunner.h
index ed89a2bc68..63d1a07337 100644
--- a/include/api/CDataFrameAnalysisRunner.h
+++ b/include/api/CDataFrameAnalysisRunner.h
@@ -103,9 +103,6 @@ class API_EXPORT CDataFrameAnalysisRunner {
     //! \return The number of columns this analysis appends.
     virtual std::size_t numberExtraColumns() const = 0;
 
-    //! \return Indicator of columns for which empty value should be treated as missing.
-    virtual TBoolVec columnsForWhichEmptyIsMissing(const TStrVec& fieldNames) const;
-
     //! Write the extra columns of \p row added by the analysis to \p writer.
     //!
     //! This should create a new object of the form:
diff --git a/include/api/CDataFrameAnalysisSpecification.h b/include/api/CDataFrameAnalysisSpecification.h
index 266e9136ce..339478a550 100644
--- a/include/api/CDataFrameAnalysisSpecification.h
+++ b/include/api/CDataFrameAnalysisSpecification.h
@@ -172,22 +172,22 @@ class API_EXPORT CDataFrameAnalysisSpecification {
     //! \note The commit of the results of the analysis is atomic per partition.
     //! \warning This assumes that there is no access to the data frame in the
     //! calling thread until the runner has finished.
-    CDataFrameAnalysisRunner* run(core::CDataFrame& frame) const;
+    CDataFrameAnalysisRunner*
+    run(core::CDataFrame& frame,
+        core::CRapidJsonConcurrentLineWriter* outputWriter = nullptr) const;
 
     //! Estimates memory usage in two cases:
     //!   1. disk is not used (the whole data frame fits in main memory)
     //!   2. disk is used (only one partition needs to be loaded to main memory)
     void estimateMemoryUsage(CMemoryUsageEstimationResultJsonWriter& writer) const;
 
-    //! \return Indicator of columns for which empty value should be treated as missing.
-    TBoolVec columnsForWhichEmptyIsMissing(const TStrVec& fieldNames) const;
-
-    //! \return shared pointer to the persistence stream.
+    //! \return The stream to which to persist state if there is one.
     TDataAdderUPtr persister() const;
 
+    //! \return The stream from which to retore state if there is one.
     TDataSearcherUPtr restoreSearcher() const;
 
-    //! Get pointer to the analysis runner.
+    //! \return The analysis runner.
     CDataFrameAnalysisRunner* runner();
 
 private:
diff --git a/include/api/CDataFrameTrainBoostedTreeClassifierRunner.h b/include/api/CDataFrameTrainBoostedTreeClassifierRunner.h
index 1568c49fc3..6bc7ea8981 100644
--- a/include/api/CDataFrameTrainBoostedTreeClassifierRunner.h
+++ b/include/api/CDataFrameTrainBoostedTreeClassifierRunner.h
@@ -40,9 +40,6 @@ class API_EXPORT CDataFrameTrainBoostedTreeClassifierRunner final
     CDataFrameTrainBoostedTreeClassifierRunner(const CDataFrameAnalysisSpecification& spec,
                                                const CDataFrameAnalysisParameters& parameters);
 
-    //! \return Indicator of columns for which empty value should be treated as missing.
-    TBoolVec columnsForWhichEmptyIsMissing(const TStrVec& fieldNames) const override;
-
     //! Write the prediction for \p row to \p writer.
     void writeOneRow(const core::CDataFrame& frame,
                      const TRowRef& row,
diff --git a/include/core/CDataFrame.h b/include/core/CDataFrame.h
index d64c5c7bb1..44f54aafac 100644
--- a/include/core/CDataFrame.h
+++ b/include/core/CDataFrame.h
@@ -450,9 +450,6 @@ class CORE_EXPORT CDataFrame final {
     //! Write the string which indicates that a value is missing.
     void missingString(std::string missing);
 
-    //! Write for which columns an empty string implies the value is missing.
-    void emptyIsMissing(TBoolVec emptyIsMissing);
-
     //! Write which columns contain categorical data.
     void categoricalColumns(TStrVec categoricalColumnNames);
 
@@ -589,11 +586,6 @@ class CORE_EXPORT CDataFrame final {
     //! The string which indicates that a category is missing.
     std::string m_MissingString;
 
-    //! Indicator vector for treating empty strings as missing values.
-    // TODO Remove once Java passes the correct value for the missing target
-    // for classification.
-    TBoolVec m_EmptyIsMissing;
-
     //! Indicator vector of the columns which contain categorical values.
     TBoolVec m_ColumnIsCategorical;
 
diff --git a/lib/api/CDataFrameAnalysisRunner.cc b/lib/api/CDataFrameAnalysisRunner.cc
index 893742d422..b81ff58e7f 100644
--- a/lib/api/CDataFrameAnalysisRunner.cc
+++ b/lib/api/CDataFrameAnalysisRunner.cc
@@ -42,10 +42,6 @@ CDataFrameAnalysisRunner::~CDataFrameAnalysisRunner() {
     this->waitToFinish();
 }
 
-TBoolVec CDataFrameAnalysisRunner::columnsForWhichEmptyIsMissing(const TStrVec& fieldNames) const {
-    return TBoolVec(fieldNames.size(), false);
-}
-
 void CDataFrameAnalysisRunner::estimateMemoryUsage(CMemoryUsageEstimationResultJsonWriter& writer) const {
     std::size_t numberRows{m_Spec.numberRows()};
     std::size_t numberColumns{m_Spec.numberColumns()};
diff --git a/lib/api/CDataFrameAnalysisSpecification.cc b/lib/api/CDataFrameAnalysisSpecification.cc
index 1a1203fa82..725ba40a39 100644
--- a/lib/api/CDataFrameAnalysisSpecification.cc
+++ b/lib/api/CDataFrameAnalysisSpecification.cc
@@ -118,14 +118,14 @@ CDataFrameAnalysisSpecification::CDataFrameAnalysisSpecification(
     rapidjson::Document specification;
     if (specification.Parse(jsonSpecification.c_str()) == false) {
         HANDLE_FATAL(<< "Input error: failed to parse analysis specification '"
-                     << jsonSpecification << "'. Please report this problem.");
+                     << jsonSpecification << "'. Please report this problem.")
     } else {
 
         auto parameters = CONFIG_READER.read(specification);
 
         for (auto name : {ROWS, COLS, MEMORY_LIMIT, THREADS}) {
             if (parameters[name].as<std::size_t>() == 0) {
-                HANDLE_FATAL(<< "Input error: '" << name << "' must be non-zero");
+                HANDLE_FATAL(<< "Input error: '" << name << "' must be non-zero")
             }
         }
         m_NumberRows = parameters[ROWS].as<std::size_t>();
@@ -207,8 +207,11 @@ CDataFrameAnalysisSpecification::makeDataFrame() {
     return result;
 }
 
-CDataFrameAnalysisRunner* CDataFrameAnalysisSpecification::run(core::CDataFrame& frame) const {
+CDataFrameAnalysisRunner*
+CDataFrameAnalysisSpecification::run(core::CDataFrame& frame,
+                                     core::CRapidJsonConcurrentLineWriter* writer) const {
     if (m_Runner != nullptr) {
+        m_Runner->instrumentation().writer(writer);
         m_Runner->run(frame);
         return m_Runner.get();
     }
@@ -218,20 +221,12 @@ CDataFrameAnalysisRunner* CDataFrameAnalysisSpecification::run(core::CDataFrame&
 void CDataFrameAnalysisSpecification::estimateMemoryUsage(CMemoryUsageEstimationResultJsonWriter& writer) const {
     if (m_Runner == nullptr) {
         HANDLE_FATAL(<< "Internal error: no runner available so can't estimate memory."
-                     << " Please report this problem.");
+                     << " Please report this problem.")
         return;
     }
     m_Runner->estimateMemoryUsage(writer);
 }
 
-TBoolVec CDataFrameAnalysisSpecification::columnsForWhichEmptyIsMissing(const TStrVec& fieldNames) const {
-    if (m_Runner == nullptr) {
-        HANDLE_FATAL(<< "Internal error: no runner available. Please report this problem.");
-        return TBoolVec(fieldNames.size(), false);
-    }
-    return m_Runner->columnsForWhichEmptyIsMissing(fieldNames);
-}
-
 void CDataFrameAnalysisSpecification::initializeRunner(const rapidjson::Value& jsonAnalysis) {
     // We pass of the interpretation of the parameters object to the appropriate
     // analysis runner.
@@ -251,7 +246,7 @@ void CDataFrameAnalysisSpecification::initializeRunner(const rapidjson::Value& j
     }
 
     HANDLE_FATAL(<< "Input error: unexpected analysis name '" << m_AnalysisName
-                 << "'. Please report this problem.");
+                 << "'. Please report this problem.")
 }
 
 CDataFrameAnalysisSpecification::TDataAdderUPtr
diff --git a/lib/api/CDataFrameAnalyzer.cc b/lib/api/CDataFrameAnalyzer.cc
index 921c5b0853..fc90c8b34c 100644
--- a/lib/api/CDataFrameAnalyzer.cc
+++ b/lib/api/CDataFrameAnalyzer.cc
@@ -132,15 +132,13 @@ void CDataFrameAnalyzer::run() {
     auto outStream = m_ResultsStreamSupplier();
     core::CRapidJsonConcurrentLineWriter outputWriter{*outStream};
 
-    CDataFrameAnalysisRunner* analysis{m_AnalysisSpecification->runner()};
-    if (analysis == nullptr) {
-        return;
+    auto analysisRunner = m_AnalysisSpecification->run(*m_DataFrame, &outputWriter);
+
+    if (analysisRunner != nullptr) {
+        this->monitorProgress(*analysisRunner, outputWriter);
+        analysisRunner->waitToFinish();
+        this->writeResultsOf(*analysisRunner, outputWriter);
     }
-    analysis->instrumentation().writer(&outputWriter);
-    m_AnalysisSpecification->run(*m_DataFrame);
-    this->monitorProgress(*analysis, outputWriter);
-    analysis->waitToFinish();
-    this->writeResultsOf(*analysis, outputWriter);
 }
 
 const CDataFrameAnalyzer::TTemporaryDirectoryPtr& CDataFrameAnalyzer::dataFrameDirectory() const {
@@ -248,8 +246,6 @@ void CDataFrameAnalyzer::captureFieldNames(const TStrVec& fieldNames) {
         TStrVec columnNames{fieldNames.begin() + m_BeginDataFieldValues,
                             fieldNames.begin() + m_EndDataFieldValues};
         m_DataFrame->columnNames(columnNames);
-        m_DataFrame->emptyIsMissing(
-            m_AnalysisSpecification->columnsForWhichEmptyIsMissing(columnNames));
         m_DataFrame->categoricalColumns(m_AnalysisSpecification->categoricalFieldNames());
         m_CapturedFieldNames = true;
     }
diff --git a/lib/api/CDataFrameTrainBoostedTreeClassifierRunner.cc b/lib/api/CDataFrameTrainBoostedTreeClassifierRunner.cc
index 3f76fd5ac7..59892c311e 100644
--- a/lib/api/CDataFrameTrainBoostedTreeClassifierRunner.cc
+++ b/lib/api/CDataFrameTrainBoostedTreeClassifierRunner.cc
@@ -96,19 +96,6 @@ CDataFrameTrainBoostedTreeClassifierRunner::CDataFrameTrainBoostedTreeClassifier
     }
 }
 
-TBoolVec CDataFrameTrainBoostedTreeClassifierRunner::columnsForWhichEmptyIsMissing(
-    const TStrVec& fieldNames) const {
-    // The only field for which empty value should be treated as missing is dependent
-    // variable which has empty value for non-training rows.
-    TBoolVec emptyAsMissing(fieldNames.size(), false);
-    auto pos = std::find(fieldNames.begin(), fieldNames.end(),
-                         this->dependentVariableFieldName());
-    if (pos != fieldNames.end()) {
-        emptyAsMissing[pos - fieldNames.begin()] = true;
-    }
-    return emptyAsMissing;
-}
-
 void CDataFrameTrainBoostedTreeClassifierRunner::writeOneRow(
     const core::CDataFrame& frame,
     const TRowRef& row,
diff --git a/lib/api/unittest/CDataFrameAnalysisRunnerTest.cc b/lib/api/unittest/CDataFrameAnalysisRunnerTest.cc
index a3e57fafd7..1e221a1d58 100644
--- a/lib/api/unittest/CDataFrameAnalysisRunnerTest.cc
+++ b/lib/api/unittest/CDataFrameAnalysisRunnerTest.cc
@@ -204,31 +204,4 @@ BOOST_AUTO_TEST_CASE(testEstimateMemoryUsageFor1000Rows) {
     testEstimateMemoryUsage(1000, "403kB", "142kB", 0);
 }
 
-void testColumnsForWhichEmptyIsMissing(const std::string& analysis,
-                                       const std::string& dependentVariableName,
-                                       const TStrVec& fieldNames,
-                                       const TStrVec& categoricalFields,
-                                       const TBoolVec& expectedEmptyIsMissing) {
-    std::string parameters{"{\"dependent_variable\": \"" + dependentVariableName + "\"}"};
-    std::string jsonSpec{api::CDataFrameAnalysisSpecificationJsonWriter::jsonString(
-        "testJob", 10000, 5, 100000000, 1, "", categoricalFields, true,
-        test::CTestTmpDir::tmpDir(), "", analysis, parameters)};
-    api::CDataFrameAnalysisSpecification spec{jsonSpec};
-    auto emptyIsMissing = spec.columnsForWhichEmptyIsMissing(fieldNames);
-    BOOST_REQUIRE_EQUAL(core::CContainerPrinter::print(expectedEmptyIsMissing),
-                        core::CContainerPrinter::print(emptyIsMissing));
-}
-
-BOOST_AUTO_TEST_CASE(testColumnsForWhichEmptyIsMissingClassification) {
-    testColumnsForWhichEmptyIsMissing("classification", "class",
-                                      {"feature_1", "feature_2", "feature_3", "class"},
-                                      {"class"}, {false, false, false, true});
-}
-
-BOOST_AUTO_TEST_CASE(testColumnsForWhichEmptyIsMissingRegression) {
-    testColumnsForWhichEmptyIsMissing("regression", "value",
-                                      {"feature_1", "feature_2", "feature_3", "value"},
-                                      {}, {false, false, false, false});
-}
-
 BOOST_AUTO_TEST_SUITE_END()
diff --git a/lib/api/unittest/CDataFrameAnalysisSpecificationTest.cc b/lib/api/unittest/CDataFrameAnalysisSpecificationTest.cc
index bfca409ac2..ca5d2d18ac 100644
--- a/lib/api/unittest/CDataFrameAnalysisSpecificationTest.cc
+++ b/lib/api/unittest/CDataFrameAnalysisSpecificationTest.cc
@@ -405,7 +405,7 @@ BOOST_AUTO_TEST_CASE(testRunAnalysis) {
             BOOST_TEST_REQUIRE(runner->instrumentation().progress() <= 1.0);
         }
 
-        LOG_DEBUG(<< "final progress = " << lastProgress);
+        LOG_TRACE(<< "final progress = " << lastProgress);
         BOOST_REQUIRE_EQUAL(1.0, runner->instrumentation().progress());
     }
 }
diff --git a/lib/api/unittest/CDataFrameAnalyzerTrainingTest.cc b/lib/api/unittest/CDataFrameAnalyzerTrainingTest.cc
index 1af987a3da..72d0e28be2 100644
--- a/lib/api/unittest/CDataFrameAnalyzerTrainingTest.cc
+++ b/lib/api/unittest/CDataFrameAnalyzerTrainingTest.cc
@@ -653,7 +653,7 @@ BOOST_AUTO_TEST_CASE(testRunBoostedTreeRegressionTrainingWithRowsMissingTargetVa
     }
     for (std::size_t i = 40; i < 50; ++i) {
         fieldValues[0] = std::to_string(feature[i]);
-        fieldValues[1] = "";
+        fieldValues[1] = core::CDataFrame::DEFAULT_MISSING_STRING;
         fieldValues[2] = std::to_string(i);
         analyzer.handleRecord(fieldNames, fieldValues);
     }
@@ -985,10 +985,8 @@ BOOST_AUTO_TEST_CASE(testCategoricalFieldsEmptyAsMissing) {
         return [expected](double actual) { return expected == actual; };
     };
 
-    auto missing = []() {
-        return [](double actual) {
-            return maths::CDataFrameUtils::isMissing(actual);
-        };
+    auto missing = [](double actual) {
+        return maths::CDataFrameUtils::isMissing(actual);
     };
 
     auto assertRow = [&](const std::size_t row_i,
@@ -1007,11 +1005,14 @@ BOOST_AUTO_TEST_CASE(testCategoricalFieldsEmptyAsMissing) {
         return std::make_unique<core::CJsonOutputStreamWrapper>(output);
     };
 
+    std::string missingString{"foo"};
+
     test::CDataFrameAnalysisSpecificationFactory specFactory;
     api::CDataFrameAnalyzer analyzer{
         specFactory.rows(1000)
             .memoryLimit(27000000)
             .predictionCategoricalFieldNames({"x1", "x2", "x5"})
+            .missingString(missingString)
             .predictionSpec(test::CDataFrameAnalysisSpecificationFactory::classification(), "x5"),
         outputWriterFactory};
 
@@ -1019,11 +1020,11 @@ BOOST_AUTO_TEST_CASE(testCategoricalFieldsEmptyAsMissing) {
     analyzer.handleRecord(fieldNames, {"x11", "x21", "0", "0", "x51", "0", ""});
     analyzer.handleRecord(fieldNames, {"x12", "x22", "1", "1", "x52", "1", ""});
     analyzer.handleRecord(fieldNames, {"", "x23", "2", "2", "x51", "2", ""});
-    analyzer.handleRecord(fieldNames, {"x14", "x24", "3", "3", "", "3", ""});
+    analyzer.handleRecord(fieldNames, {"x14", "x24", "3", "3", missingString, "3", ""});
     analyzer.handleRecord(fieldNames, {"x15", "x25", "4", "4", "x51", "4", ""});
     analyzer.handleRecord(fieldNames, {"x11", "x26", "5", "5", "x52", "5", ""});
-    analyzer.handleRecord(fieldNames, {"x12", "", "6", "6", "", "6", ""});
-    analyzer.handleRecord(fieldNames, {"x13", "x21", "7", "7", "", "7", ""});
+    analyzer.handleRecord(fieldNames, {"x12", "", "6", "6", missingString, "6", ""});
+    analyzer.handleRecord(fieldNames, {"x13", "x21", "7", "7", missingString, "7", ""});
     analyzer.handleRecord(fieldNames, {"x14", "x22", "8", "8", "x51", "8", ""});
     analyzer.handleRecord(fieldNames, {"", "x23", "9", "9", "x52", "9", ""});
     analyzer.receivedAllRows();
@@ -1036,11 +1037,11 @@ BOOST_AUTO_TEST_CASE(testCategoricalFieldsEmptyAsMissing) {
         assertRow(0, {eq(0.0), eq(0.0), eq(0.0), eq(0.0), eq(0.0)}, rows[0]);
         assertRow(1, {eq(1.0), eq(1.0), eq(1.0), eq(1.0), eq(1.0)}, rows[1]);
         assertRow(2, {eq(2.0), eq(2.0), eq(2.0), eq(2.0), eq(0.0)}, rows[2]);
-        assertRow(3, {eq(3.0), eq(3.0), eq(3.0), eq(3.0), missing()}, rows[3]);
+        assertRow(3, {eq(3.0), eq(3.0), eq(3.0), eq(3.0), missing}, rows[3]);
         assertRow(4, {eq(4.0), eq(4.0), eq(4.0), eq(4.0), eq(0.0)}, rows[4]);
         assertRow(5, {eq(0.0), eq(5.0), eq(5.0), eq(5.0), eq(1.0)}, rows[5]);
-        assertRow(6, {eq(1.0), eq(6.0), eq(6.0), eq(6.0), missing()}, rows[6]);
-        assertRow(7, {eq(5.0), eq(0.0), eq(7.0), eq(7.0), missing()}, rows[7]);
+        assertRow(6, {eq(1.0), eq(6.0), eq(6.0), eq(6.0), missing}, rows[6]);
+        assertRow(7, {eq(5.0), eq(0.0), eq(7.0), eq(7.0), missing}, rows[7]);
         assertRow(8, {eq(3.0), eq(1.0), eq(8.0), eq(8.0), eq(0.0)}, rows[8]);
         assertRow(9, {eq(2.0), eq(2.0), eq(9.0), eq(9.0), eq(1.0)}, rows[9]);
     });
diff --git a/lib/core/CDataFrame.cc b/lib/core/CDataFrame.cc
index 3f05c330d2..c65baba8ab 100644
--- a/lib/core/CDataFrame.cc
+++ b/lib/core/CDataFrame.cc
@@ -127,7 +127,6 @@ CDataFrame::CDataFrame(bool inMainMemory,
       m_ReadAndWriteToStoreSyncStrategy{readAndWriteToStoreSyncStrategy},
       m_WriteSliceToStore{writeSliceToStore}, m_ColumnNames(numberColumns),
       m_CategoricalColumnValues(numberColumns), m_MissingString{DEFAULT_MISSING_STRING},
-      m_EmptyIsMissing(numberColumns, false),
       m_ColumnIsCategorical(numberColumns, false) {
 }
 
@@ -169,7 +168,6 @@ void CDataFrame::resizeColumns(std::size_t numberThreads, std::size_t numberColu
     this->reserve(numberThreads, numberColumns);
     m_ColumnNames.resize(numberColumns);
     m_CategoricalColumnValues.resize(numberColumns);
-    m_EmptyIsMissing.resize(numberColumns, false);
     m_ColumnIsCategorical.resize(numberColumns, false);
     m_NumberColumns = numberColumns;
 }
@@ -215,19 +213,13 @@ CDataFrame::TRowFuncVecBoolPr CDataFrame::writeColumns(std::size_t numberThreads
 void CDataFrame::parseAndWriteRow(const TStrCRng& columnValues, const std::string* hash) {
 
     auto stringToValue = [this](bool isCategorical, TStrSizeUMap& categoryLookup,
-                                TStrVec& categories, bool emptyIsMissing,
-                                const std::string& columnValue) {
+                                TStrVec& categories, const std::string& columnValue) {
         if (columnValue == m_MissingString) {
             ++m_MissingValueCount;
             return core::CFloatStorage{valueOfMissing()};
         }
 
         if (isCategorical) {
-            // TODO Remove when Java passes special missing value string.
-            if (columnValue.empty() && emptyIsMissing) {
-                return core::CFloatStorage{valueOfMissing()};
-            }
-
             // This encodes in a format suitable for efficient storage. The
             // actual encoding approach is chosen when the analysis runs.
             std::size_t id;
@@ -257,11 +249,7 @@ void CDataFrame::parseAndWriteRow(const TStrCRng& columnValues, const std::strin
         // otherwise we must impute or exit with failure.
 
         double value;
-        if (columnValue.empty()) {
-            // TODO Remove when Java passes special missing value string.
-            ++m_MissingValueCount;
-            return core::CFloatStorage{valueOfMissing()};
-        } else if (core::CStringUtils::stringToTypeSilent(columnValue, value) == false) {
+        if (core::CStringUtils::stringToTypeSilent(columnValue, value) == false) {
             ++m_BadValueCount;
             return core::CFloatStorage{valueOfMissing()};
         }
@@ -278,9 +266,9 @@ void CDataFrame::parseAndWriteRow(const TStrCRng& columnValues, const std::strin
 
     this->writeRow([&](TFloatVecItr columns, std::int32_t& docHash) {
         for (std::size_t i = 0; i < columnValues.size(); ++i, ++columns) {
-            *columns = stringToValue(
-                m_ColumnIsCategorical[i], m_CategoricalColumnValueLookup[i],
-                m_CategoricalColumnValues[i], m_EmptyIsMissing[i], columnValues[i]);
+            *columns = stringToValue(m_ColumnIsCategorical[i],
+                                     m_CategoricalColumnValueLookup[i],
+                                     m_CategoricalColumnValues[i], columnValues[i]);
         }
         docHash = 0;
         if (hash != nullptr &&
@@ -312,16 +300,6 @@ void CDataFrame::missingString(std::string missing) {
     m_MissingString = std::move(missing);
 }
 
-void CDataFrame::emptyIsMissing(TBoolVec emptyIsMissing) {
-    if (emptyIsMissing.size() != m_NumberColumns) {
-        HANDLE_FATAL(<< "Internal error: expected '" << m_NumberColumns
-                     << "' 'empty is missing' column indicator values but got "
-                     << CContainerPrinter::print(emptyIsMissing));
-    } else {
-        m_EmptyIsMissing = std::move(emptyIsMissing);
-    }
-}
-
 void CDataFrame::categoricalColumns(TStrVec categoricalColumnNames) {
     std::sort(categoricalColumnNames.begin(), categoricalColumnNames.end());
     for (std::size_t i = 0; i < m_ColumnNames.size(); ++i) {
@@ -387,7 +365,6 @@ std::size_t CDataFrame::memoryUsage() const {
     memory += CMemory::dynamicSize(m_CategoricalColumnValues);
     memory += CMemory::dynamicSize(m_CategoricalColumnValueLookup);
     memory += CMemory::dynamicSize(m_MissingString);
-    memory += CMemory::dynamicSize(m_EmptyIsMissing);
     memory += CMemory::dynamicSize(m_ColumnIsCategorical);
     memory += CMemory::dynamicSize(m_Slices);
     memory += CMemory::dynamicSize(m_Writer);