Update README.md

README.md

@@ -117,7 +117,7 @@ The predict method accepts the following arguments.
 
 The `predict_contributions` method will predict with the fitted booster on new data, returning the feature contribution matrix. The last column is the bias term.
  - `X` ***(FrameLike)***: Either a pandas DataFrame, or a 2 dimensional numpy array, with numeric data.
- - `method` ***(str, optional)***: Method to calculate the contributions, if "average" is specified, the average internal node values are calculated, this is equivalent to the `approx_contribs` parameter in XGBoost. The other supported method is "weight", this will use the internal leaf weights, to calculate the contributions. This is the same as what is described by Saabas [here](https://blog.datadive.net/interpreting-random-forests/). The final method is "branch-difference", this experimental method will calculate contributions by the weight of the node the record will travel down, and the weight of the other non-missing branch. This last method does not have the property where the contributions summed is equal to the final prediction of the model.
+ - `method` ***(str, optional)***: Method to calculate the contributions, if "average" is specified, the average internal node values are calculated, this is equivalent to the `approx_contribs` parameter in XGBoost. The other supported method is "weight", this will use the internal leaf weights, to calculate the contributions. This is the same as what is described by Saabas [here](https://blog.datadive.net/interpreting-random-forests/). The final method is "branch-difference", this experimental method will calculate contributions by subtracting the weight of the node the record will travel down by the weight of the other non-missing branch. This last method does not have the property where the contributions summed is equal to the final prediction of the model.
  - `parallel` ***(Optional[bool], optional)***: Optionally specify if the predict function should run in parallel on multiple threads. If `None` is passed, the `parallel` attribute of the booster will be used. Defaults to `None`.
 
 When predicting with the data, the maximum iteration that will be used when predicting can be set using the `set_prediction_iteration` method. If `early_stopping_rounds` has been set, this will default to the best iteration, otherwise all of the trees will be used. It accepts a single value.