diff --git a/docs/freqai-configuration.md b/docs/freqai-configuration.md
index 6e2ed8379..090fa8415 100644
--- a/docs/freqai-configuration.md
+++ b/docs/freqai-configuration.md
@@ -160,7 +160,7 @@ Below are the values you can expect to include/use inside a typical strategy dat
 |------------|-------------|
 | `df['&*']` | Any dataframe column prepended with `&` in `set_freqai_targets()` is treated as a training target (label) inside FreqAI (typically following the naming convention `&-s*`). For example, to predict the close price 40 candles into the future, you would set `df['&-s_close'] = df['close'].shift(-self.freqai_info["feature_parameters"]["label_period_candles"])` with `"label_period_candles": 40` in the config. FreqAI makes the predictions and gives them back under the same key (`df['&-s_close']`) to be used in `populate_entry/exit_trend()`. <br> **Datatype:** Depends on the output of the model.
 | `df['&*_std/mean']` | Standard deviation and mean values of the defined labels during training (or live tracking with `fit_live_predictions_candles`). Commonly used to understand the rarity of a prediction (use the z-score as shown in `templates/FreqaiExampleStrategy.py` and explained [here](#creating-a-dynamic-target-threshold) to evaluate how often a particular prediction was observed during training or historically with `fit_live_predictions_candles`). <br> **Datatype:** Float.
-| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers()` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
+| `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
 | `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
 | `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%`. <br> **Datatype:** Depends on the output of the model.
 
diff --git a/docs/strategy_migration.md b/docs/strategy_migration.md
index 353da0ccb..d32d5880c 100644
--- a/docs/strategy_migration.md
+++ b/docs/strategy_migration.md
@@ -801,7 +801,7 @@ class MyCoolFreqaiModel(BaseRegressionModel):
             dk.DI_values = dk.feature_pipeline["di"].di_values
         else:
             dk.DI_values = np.zeros(len(outliers.index))
-        dk.do_predict = outliers.to_numpy()
+        dk.do_predict = outliers
 
         # ... your custom code
         return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/base_models/BaseClassifierModel.py b/freqtrade/freqai/base_models/BaseClassifierModel.py
index f35b07e66..810fb6894 100644
--- a/freqtrade/freqai/base_models/BaseClassifierModel.py
+++ b/freqtrade/freqai/base_models/BaseClassifierModel.py
@@ -121,6 +121,6 @@ class BaseClassifierModel(IFreqaiModel):
             dk.DI_values = dk.feature_pipeline["di"].di_values
         else:
             dk.DI_values = np.zeros(len(outliers.index))
-        dk.do_predict = outliers.to_numpy()
+        dk.do_predict = outliers
 
         return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/base_models/BasePyTorchClassifier.py b/freqtrade/freqai/base_models/BasePyTorchClassifier.py
index c47c5069a..040549ac1 100644
--- a/freqtrade/freqai/base_models/BasePyTorchClassifier.py
+++ b/freqtrade/freqai/base_models/BasePyTorchClassifier.py
@@ -95,7 +95,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
             dk.DI_values = dk.feature_pipeline["di"].di_values
         else:
             dk.DI_values = np.zeros(len(outliers.index))
-        dk.do_predict = outliers.to_numpy()
+        dk.do_predict = outliers
 
         return (pred_df, dk.do_predict)
 
diff --git a/freqtrade/freqai/base_models/BasePyTorchRegressor.py b/freqtrade/freqai/base_models/BasePyTorchRegressor.py
index 325743134..71d384653 100644
--- a/freqtrade/freqai/base_models/BasePyTorchRegressor.py
+++ b/freqtrade/freqai/base_models/BasePyTorchRegressor.py
@@ -56,7 +56,7 @@ class BasePyTorchRegressor(BasePyTorchModel):
             dk.DI_values = dk.feature_pipeline["di"].di_values
         else:
             dk.DI_values = np.zeros(len(outliers.index))
-        dk.do_predict = outliers.to_numpy()
+        dk.do_predict = outliers
         return (pred_df, dk.do_predict)
 
     def train(
diff --git a/freqtrade/freqai/base_models/BaseRegressionModel.py b/freqtrade/freqai/base_models/BaseRegressionModel.py
index 2e07d3fb7..6f83756dc 100644
--- a/freqtrade/freqai/base_models/BaseRegressionModel.py
+++ b/freqtrade/freqai/base_models/BaseRegressionModel.py
@@ -115,6 +115,6 @@ class BaseRegressionModel(IFreqaiModel):
             dk.DI_values = dk.feature_pipeline["di"].di_values
         else:
             dk.DI_values = np.zeros(len(outliers.index))
-        dk.do_predict = outliers.to_numpy()
+        dk.do_predict = outliers
 
         return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 4ca5467b6..b053e276f 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -1013,5 +1013,5 @@ class IFreqaiModel(ABC):
             dk.DI_values = dk.feature_pipeline["di"].di_values
         else:
             dk.DI_values = np.zeros(len(outliers.index))
-        dk.do_predict = outliers.to_numpy()
+        dk.do_predict = outliers
         return
diff --git a/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
index bf78488ff..857c1edb8 100644
--- a/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
+++ b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
@@ -137,7 +137,7 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
             dk.DI_values = dk.feature_pipeline["di"].di_values
         else:
             dk.DI_values = np.zeros(len(outliers.index))
-        dk.do_predict = outliers.to_numpy()
+        dk.do_predict = outliers
 
         if x.shape[1] > 1:
             zeros_df = pd.DataFrame(np.zeros((x.shape[1] - len(pred_df), len(pred_df.columns))),