fix typos

docs/freqai.md

@@ -109,8 +109,8 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `indicator_max_period_candles` | **No longer used**. User must use the strategy set `startup_candle_count` which defines the maximum *period* used in `populate_any_indicators()` for indicator creation (timeframe independent). FreqAI uses this information in combination with the maximum timeframe to calculate how many data points it should download so that the first data point does not have a NaN <br> **Datatype:** positive integer.
 | `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set. <br> **Datatype:** List of positive integers.
 | `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](#stratifying-the-data-for-training-and-testing-the-model) <br> **Datatype:** Positive integer.
-| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
-| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features.<br> **Datatype:** Boolean.<br> **Datatype:** Boolean, defaults to `0`
+| `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis) <br> **Datatype:** Boolean. defaults to `false`.
+| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features.<br> **Datatype:** Integer, defaults to `0`.
 | `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** Positive float (typically < 1).
 | `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
 | `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.

freqtrade/freqai/data_drawer.py

@@ -377,7 +377,7 @@ class FreqaiDataDrawer:
         if self.config.get("freqai", {}).get("purge_old_models", False):
             self.purge_old_models()
 
-    def save_metaddata(self, dk: FreqaiDataKitchen) -> None:
+    def save_metadata(self, dk: FreqaiDataKitchen) -> None:
         """
         Saves only metadata for backtesting studies if user prefers
         not to save model data. This saves tremendous amounts of space

freqtrade/freqai/freqai_interface.py

@@ -292,7 +292,7 @@ class IFreqaiModel(ABC):
                         self.dd.save_data(self.model, pair, dk)
                     else:
                         logger.info('Saving metadata to disk.')
-                        self.dd.save_metaddata(dk)
+                        self.dd.save_metadata(dk)
                 else:
                     self.model = self.dd.load_data(pair, dk)