diff --git a/tmva/pymva/src/MethodPyGTB.cxx b/tmva/pymva/src/MethodPyGTB.cxx
index 7b994722e0b35..56b76ba06b6b3 100644
--- a/tmva/pymva/src/MethodPyGTB.cxx
+++ b/tmva/pymva/src/MethodPyGTB.cxx
@@ -65,7 +65,7 @@ MethodPyGTB::MethodPyGTB(const TString &jobName,
                          DataSetInfo &dsi,
                          const TString &theOption) :
    PyMethodBase(jobName, Types::kPyGTB, methodTitle, dsi, theOption),
-   fLoss("deviance"),
+   fLoss("log_loss"),
    fLearningRate(0.1),
    fNestimators(100),
    fSubsample(1.0),
@@ -85,7 +85,7 @@ MethodPyGTB::MethodPyGTB(const TString &jobName,
 //_______________________________________________________________________
 MethodPyGTB::MethodPyGTB(DataSetInfo &theData, const TString &theWeightFile)
    : PyMethodBase(Types::kPyGTB, theData, theWeightFile),
-   fLoss("deviance"),
+   fLoss("log_loss"),
    fLearningRate(0.1),
    fNestimators(100),
    fSubsample(1.0),
@@ -122,9 +122,9 @@ void MethodPyGTB::DeclareOptions()
 {
    MethodBase::DeclareCompatibilityOptions();
 
-   DeclareOptionRef(fLoss, "Loss", "{'deviance', 'exponential'}, optional (default='deviance')\
-      loss function to be optimized. 'deviance' refers to\
-      deviance (= logistic regression) for classification\
+   DeclareOptionRef(fLoss, "Loss", "{'log_loss', 'exponential'}, optional (default='log_loss')\
+      loss function to be optimized. 'log_loss' refers to\
+      logistic loss for classification\
       with probabilistic outputs. For loss 'exponential' gradient\
       boosting recovers the AdaBoost algorithm.");
 
@@ -197,9 +197,9 @@ void MethodPyGTB::DeclareOptions()
 // Check options and load them to local python namespace
 void MethodPyGTB::ProcessOptions()
 {
-   if (fLoss != "deviance" && fLoss != "exponential") {
+   if (fLoss != "log_loss" && fLoss != "exponential") {
       Log() << kFATAL << Form("Loss = %s ... that does not work!", fLoss.Data())
-            << " The options are 'deviance' or 'exponential'." << Endl;
+            << " The options are 'log_loss' or 'exponential'." << Endl;
    }
    pLoss = Eval(Form("'%s'", fLoss.Data()));
    PyDict_SetItemString(fLocalNS, "loss", pLoss);
diff --git a/tmva/pymva/src/MethodPyRandomForest.cxx b/tmva/pymva/src/MethodPyRandomForest.cxx
index 2275bab511e4e..3424d80164321 100644
--- a/tmva/pymva/src/MethodPyRandomForest.cxx
+++ b/tmva/pymva/src/MethodPyRandomForest.cxx
@@ -69,7 +69,7 @@ MethodPyRandomForest::MethodPyRandomForest(const TString &jobName,
    fMinSamplesSplit(2),
    fMinSamplesLeaf(1),
    fMinWeightFractionLeaf(0),
-   fMaxFeatures("'auto'"),
+   fMaxFeatures("'sqrt'"),
    fMaxLeafNodes("None"),
    fBootstrap(kTRUE),
    fOobScore(kFALSE),
@@ -90,7 +90,7 @@ MethodPyRandomForest::MethodPyRandomForest(DataSetInfo &theData, const TString &
    fMinSamplesSplit(2),
    fMinSamplesLeaf(1),
    fMinWeightFractionLeaf(0),
-   fMaxFeatures("'auto'"),
+   fMaxFeatures("'sqrt'"),
    fMaxLeafNodes("None"),
    fBootstrap(kTRUE),
    fOobScore(kFALSE),
@@ -234,7 +234,8 @@ void MethodPyRandomForest::ProcessOptions()
    pMinWeightFractionLeaf = Eval(Form("%f", fMinWeightFractionLeaf));
    PyDict_SetItemString(fLocalNS, "minWeightFractionLeaf", pMinWeightFractionLeaf);
 
-   if (fMaxFeatures == "auto" || fMaxFeatures == "sqrt" || fMaxFeatures == "log2"){
+   if (fMaxFeatures == "auto") fMaxFeatures = "sqrt"; // change in API from v 1.11
+   if (fMaxFeatures == "sqrt" || fMaxFeatures == "log2"){
       fMaxFeatures = Form("'%s'", fMaxFeatures.Data());
    }
    pMaxFeatures = Eval(fMaxFeatures);
@@ -428,9 +429,9 @@ std::vector<Double_t> MethodPyRandomForest::GetMvaValues(Long64_t firstEvt, Long
 
    Py_DECREF(pEvent);
    Py_DECREF(result);
-   
+
    if (logProgress) {
-      Log() << kINFO 
+      Log() << kINFO
             << "Elapsed time for evaluation of " << nEvents <<  " events: "
             << timer.GetElapsedTime() << "       " << Endl;
    }