diff --git a/docs/mlforecaster.md b/docs/mlforecaster.md
index a458013f..fe80381f 100644
--- a/docs/mlforecaster.md
+++ b/docs/mlforecaster.md
@@ -65,7 +65,7 @@ A quick prediction graph using the fitted model should be available in the webui
 
 Visually the prediction looks quite acceptable but we need to evaluate this further. For this we can use the `"perform_backtest": True` option to perform a backtest evaluation using this syntax:
 ```
-curl -i -H "Content-Type:application/json" -X POST -d '{"perform_backtest": True}' http://localhost:5000/action/forecast-model-fit
+curl -i -H "Content-Type:application/json" -X POST -d '{"perform_backtest": "True"}' http://localhost:5000/action/forecast-model-fit
 ```
 
 The results of the backtest will be shown in the logs:
@@ -164,4 +164,4 @@ In the future we may test to expand using other possible known future covariates
 ## Going further?
 This class can be gebneralized to actually forecasting any given sensor variable present in Home Assistant. It has been tested and the main initial motivation for this development was for a better load power consumption forecasting. But in reality is has been coded in a flexible way so that you can control what variable is used, how many lags, the amount of data used to train the model, etc.
 
-So you can really go further and try to forecast other types of variables and possible use the results for some interesting automations in Home Assistant. If doing this, was is important is to evaluate the pertinence of the obtained forecasts. The hope is that the tools proposed here can be used for that purpose.
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+So you can really go further and try to forecast other types of variables and possible use the results for some interesting automations in Home Assistant. If doing this, was is important is to evaluate the pertinence of the obtained forecasts. The hope is that the tools proposed here can be used for that purpose.