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@@ -19,7 +19,7 @@ PerpetualBooster is a gradient boosting machine (GBM) algorithm which doesn't ne
 
 ## Benchmark
 
-Hyperparameter optimization usually takes 100 iterations with plain GBM algorithms. PerpetualBooster achieves the same accuracy in the single run. Thus, it achieves around 100x speed-up at the same accuracy with different `budget` levels and with different datasets. The speed-up might be slightly lower or significantly higher than 100x depending on the dataset.
+Hyperparameter optimization usually takes 100 iterations with plain GBM algorithms. PerpetualBooster achieves the same accuracy in a single run. Thus, it achieves around 100x speed-up at the same accuracy with different `budget` levels and with different datasets. The speed-up might be slightly lower or significantly higher than 100x depending on the dataset.
 
 The following table summarizes the results for the [California Housing](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html) dataset (regression):