diff --git a/examples/Feature_Selection.ipynb b/examples/Feature_Selection.ipynb
index 39264e7..f8f0d99 100644
--- a/examples/Feature_Selection.ipynb
+++ b/examples/Feature_Selection.ipynb
@@ -2230,7 +2230,7 @@
     "from lumin.optimisation.features import auto_filter_on_linear_correlation\n",
     "\n",
     "filtered_feats = auto_filter_on_linear_correlation(train_df=train_df.loc[trn_idxs], val_df=train_df.loc[val_idxs],\n",
-    "                                                   check_feats=hl_feats, subsample_rate=0.01, n_rfs=5,\n",
+    "                                                   check_feats=hl_feats, subsample_rate=subsample_rate, n_rfs=5,\n",
     "                                                   corr_threshold=0.8, rf_params=rf_params, optimise_rf=False,\n",
     "                                                   objective='classification', targ_name='gen_target',\n",
     "                                                   wgt_name='gen_weight', strat_key='gen_target')"
@@ -2882,7 +2882,7 @@
     "                                                  check_feats=import_feats,\n",
     "                                                  objective='classification', targ_name='gen_target',\n",
     "                                                  strat_key='gen_target', wgt_name='gen_weight',\n",
-    "                                                  subsample_rate=0.3,\n",
+    "                                                  subsample_rate=subsample_rate,\n",
     "                                                  rf_params=OrderedDict({'min_samples_leaf': [2,4,8,16,32],\n",
     "                                                                         'max_features': [0.3,0.5,0.7,0.9]}),\n",
     "                                                  optimise_rf=True)"
diff --git a/lumin/nn/callbacks/cyclic_callbacks.py b/lumin/nn/callbacks/cyclic_callbacks.py
index 024bd46..6632e27 100644
--- a/lumin/nn/callbacks/cyclic_callbacks.py
+++ b/lumin/nn/callbacks/cyclic_callbacks.py
@@ -3,7 +3,7 @@
 from fastcore.all import store_attr
 
 from .callback import Callback, OldCallback
-from ..models.abs_model import AbsModel, OldAbsModel
+from ..models.abs_model import OldAbsModel
 from ...plotting.plot_settings import PlotSettings
 
 import seaborn as sns
diff --git a/lumin/nn/callbacks/monitors.py b/lumin/nn/callbacks/monitors.py
index 54fe95d..0daec1d 100644
--- a/lumin/nn/callbacks/monitors.py
+++ b/lumin/nn/callbacks/monitors.py
@@ -1,6 +1,5 @@
 from fastcore.all import store_attr
 import math
-from typing import Optional
 import numpy as np
 from fastprogress.fastprogress import IN_NOTEBOOK
 from IPython.display import display
@@ -10,8 +9,6 @@
 import seaborn as sns
 
 from .callback import Callback
-from ..models.abs_model import AbsModel
-from ...plotting.plot_settings import PlotSettings
 
 __all__ = ['EarlyStopping', 'SaveBest', 'MetricLogger']
 
diff --git a/lumin/nn/data/fold_yielder.py b/lumin/nn/data/fold_yielder.py
index 74ea48f..3050c1c 100644
--- a/lumin/nn/data/fold_yielder.py
+++ b/lumin/nn/data/fold_yielder.py
@@ -75,7 +75,7 @@ def columns(self) -> List[str]:
 
         return [k for k in self.foldfile["fold_0"].keys()]
 
-    def add_ignore(self, feats:List[str]) -> None:
+    def add_ignore(self, feats:Union[str,List[str]]) -> None:
         r'''
         Add features to ignored features.
 
@@ -83,6 +83,7 @@ def add_ignore(self, feats:List[str]) -> None:
             feats: list of feature names to ignore
         '''
 
+        if not is_listy(feats): feats = [feats]
         self._ignore_feats += feats
         self.cont_feats = [f for f in self.cont_feats if f not in self._ignore_feats]
         self.cat_feats  = [f for f in self.cat_feats  if f not in self._ignore_feats]
diff --git a/lumin/nn/models/model.py b/lumin/nn/models/model.py
index 3204fe1..5c74d7f 100644
--- a/lumin/nn/models/model.py
+++ b/lumin/nn/models/model.py
@@ -173,7 +173,7 @@ def fit(self, n_epochs:int, fy:FoldYielder, bs:int, bulk_move:bool=True, train_o
         elif not is_listy(cbs): cbs = [cbs]
         cyclic_cbs,loss_cbs = [],[]
         for c in cbs:
-            if isinstance(c, AbsCyclicCallback): cyclic_cbs.append(c)  # CBs that might prrevent a model from stopping training due to a hyper-param cycle
+            if isinstance(c, AbsCyclicCallback): cyclic_cbs.append(c)  # CBs that might prevent a model from stopping training due to a hyper-param cycle
             if hasattr(c, "get_loss"): loss_cbs.append(c)  # CBs that produce alternative losses that should be considered
 
         self.fit_params = FitParams(cbs=cbs, cyclic_cbs=cyclic_cbs, loss_cbs=loss_cbs, stop=False, n_epochs=n_epochs, fy=fy, val_idx=val_idx, bs=bs,