Updating README.md

README.md

@@ -5,6 +5,13 @@ A minimal example of using Bayesian Optimization to tune the learning rate of a
 
 The contents of this repository can be run in [Google Colaboratory](https://colab.research.google.com/github/stwerner97/minimal-bayesopt/blob/main/bayesopt.ipynb) or by cloning the repository.
 
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+Each plot shows the parameters sampled so far and their objective value (F1 validation score). The cross highlights the best performing parameter. The black line and orange area around it show the posterior mean and uncertainty estimates, respectively. Additionally, we show functions samples from the posterior in grey. Below, the acquisition function landscape (here: expected improvement) is shown.
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 ## Installation
 ### From Source
 The simplest option is to install the dependencies of `minimal-bayesopt` using pip: