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Final_evaluation.md
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@ -20,15 +20,15 @@ Apart from Machine Learning Algorithms used in project there are also many diffe
## Machine Learning Algorithms ## Machine Learning Algorithms
Project in its current state uses Machine Learning Algorithms to solve 2 problems. Light Gradient-Boosted Trees are used for both problems: Project in its current state uses Machine Learning Algorithms to solve 2 problems. Light Gradient-Boosted Trees are used for both problems:
-Choosing a proper fertilizer which should be applied to current tile, based on few variables like nutrients in soil. 1. Choosing a proper fertilizer which should be applied to current tile, based on few variables like nutrients in soil.
Applying proper fertilizer boosts production rate of a crop (rate of growth of a crop). This part was done by Oskar Nastały. Applying proper fertilizer boosts production rate of a crop (rate of growth of a crop). This part was done by Oskar Nastały.
-Calculating production rate of a tile based on rainfall and few other variabels. Noise map is generated and used to simulate dynamically changing rainfall. 2. Calculating production rate of a tile based on rainfall and few other variabels. Noise map is generated and used to simulate dynamically changing rainfall.
Then once a day AI is used to calculate base production rate multiplier. This part was done by Joel Städe. Then once a day AI is used to calculate base production rate multiplier. This part was done by Joel Städe.
## Examples ## Examples
![Clouds]() ![Clouds](https://git.wmi.amu.edu.pl/s425077/PotatoPlan/raw/Joel-ML/Evaluation_examples/Example_clouds.PNG)
![UI]() ![UI](https://git.wmi.amu.edu.pl/s425077/PotatoPlan/raw/Joel-ML/Evaluation_examples/Example_UI.PNG)
Final evaluation doc. Final evaluation doc.
WIP WIP