readme edit

readme.md

@@ -78,9 +78,10 @@ After running this, we can retrieve the best optuna trial and build a model out
 ```python
 sampled_model = metatune.build_sampled_model(study.best_trial)
 ```
-**Note** that the models returned are purely sci-kit-learn models, thus the reason the regular `fit(...)` and `predict(...)` methods can be called on them.
+**NOTE** that the models returned are purely sci-kit-learn models, thus the reason the regular `fit(...)` and `predict(...)` methods can be called on them.
 
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+
 **NOTE:** A thing to note is that for large datasets, hyperparameter search ought to be used on a subset of the data rather than the entire dataset. The reason for this being that the aim of hyperparameter tuning is not fit all of the data, but rather to sample the parameters that best model the data. After tuning, you can instantiate the sampled model with the corresponding sampled parameters and fine-tune the model on the large dataset, this way you avoid the unnecessary computation required to model the entirety of the available dataset.
 
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