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Is decision tree learning a deterministic algorithm? Given a fixed dataset, does it always produce a tree of a same topology? Generally, yes. Most decision tree learners, like the common ID3 and C4.5/C5.0 algorithms, are deterministic. At each step, the learners consider all possible feature that have not yet been used to split the data, and find the splits ...


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Decision Tree learners, on their own, are not a good way to deal with imbalanced data. The most commonly used algorithms, by default, make no attempt to address this problem. If you look carefully at the three sources you post, you will find that they actually all agree on this point. Two of the sources actually propose methods of addressing this ...


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There are two main things to consider for dealing with imbalanced data: During Training: Undersampling the majority class (healthy patients) so that the model is not that biased to predicting healthy During Evaluation: Using a suitable metric to try to evaluate your model and try to optimize on when you are fine-tuning your random-forest. For imbalanced ...


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A random forest is a collection of classification trees. If more than 50% of these trees predict class A (and not class B), the random forest will predict class A. What you can do is lower the percentage needed to classify it as class A (in your case, patient has the virus). This way, you can tell your random forest to predict class A if only 20% (or 10%, ...


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This is a sign of overfitting. As you make your trees deeper, it becomes possible to "memorize" the data: each leaf of the tree is just a single point. The trees begin to learn patterns that do not exist. When you try out these patterns on new data (which is what cross-validation is imitating), then the patterns do not work, and your model fails to ...


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Scaling only makes sense when there is something that reacts to that scale. Decision Trees though, just make a cut at a certain number. Imagine: For a feature that goes from 0 to 100 a cut at 50 may be improving performance. Scaling this down to 0 to 1 making the cut a 0.5 doesn't change a thing. Now on the other hand NN have some kind of activation function ...


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To gain a good understanding of this, I recommend first reading about the trade-off between bias and variance in ML and AI methods. A great article on this topic that I recommend as a light mathematical introduction is this: https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229 In short: Bias represents the models effort to ...


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Note the X index is training set size. For the first and second case, teh training set size starts at 0(or 1). The model will overfit certainly at that data size. When data size increases, the model overfits less and less and eventually the model have enough data samples that it won't overfit. The data size continue to increase and the model performance ...


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What makes a system deterministic is not the objective of the algorithm or the variability of the data set or lack thereof. It is not the system's academic origin or the label we assign to it or even whether it is predictable that drives whether it is deterministic. A system is deterministic if, given perfectly accurate and comprehensive knowledge of the ...


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The number of estimators in Random Forest is a hyper-parameter. If you are using SKLearn's Random Classifier you can use one of the following techniques to find a (near) optimal hyperparameter settings (Note:You can tweak other hyperparameters like min_leaf_size etc as well with this approach); GridSearchCV You can specify a grid of all the hyperparameters ...


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