Finish report for project 3.
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# Reports
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# Reports
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- [Report 1](./martingale/martingale.md)
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- [Report 1](./martingale/martingale.md)
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- [Report 2](./optimize_something/readme.md)
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- [Report 2](./optimize_something/optimize_something.md)
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- [Report 3](#)
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- [Report 3](./assess_learners/assess_learners.md)
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- [Report 4](#)
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# Report
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## Experiment 1
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Significant overfitting occurs for leaf sizes smaller than five. The chart shows
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that the root-mean-square-error is significantly higher for the test data
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(`rmse_out`) for leaf sizes smaller than five.
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Between five and nine, the error for the test data is only slightly higher, so
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there is small overfitting. Beyond that, the errors increase, and the error for
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the test data is lower than for the training data. In other words, there is no
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more overfitting for leaf sizes greater than nine.
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![](figure_1.png)
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## Experiment 2
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For all bag sizes, the difference of the RMSE for the training data and the test
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data is smaller than without bagging. The test data still has a lower RMSE up to
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a leaf size of five. For greater leaf sizes, the RMSE for the test data is
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smaller than for the training data for all bag sizes, so there is no
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overfitting.
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![](figure_2.png)
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![](figure_3.png)
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![](figure_4.png)
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![](figure_5.png)
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## Experiment 3
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The Random Tree learner has a correlation of one for the training data. In other
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words, it fits the training data perfectly. Consequently, the correlation for
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the test data is worse than for the Decision Tree learner. The DT learner has a
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higher correlation than the RT for all other leaf sizes, both for the training
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and the test data.
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![](figure_6.png)
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![](figure_7.png)
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