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For vision tasks, neural network models almost always include a number of layers that pool and convolute. The convolutions, in particular, are very useful - they can make the model generalize better to inputs and maintain performance when inputs have undergone certain linear transformations (e.g. some scaling or a translation along the x-axis). These ...


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Decision tree nodes are split bases on the number of data samples, these numbers indicate the number of data samples they are fit to. In your case samples = 256. It is further split into two nodes of 154 and 102.


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What this is talking about is how much a machine learning algorithm is good at "memorizing" the data. Decision trees, for their nature, tend to overfit very easily, this is because they can separate the space along very non-linear curves, especially if you get a very deep tree. Simpler algorithms, on the other hand, tend to separate the space along linear ...


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Yes, if you can assume that your data is separable on the features given then ID3 will find a decision tree for it (Note: this will not necessarily be an optimal tree, or even a good tree). To understand why let's look at a proof. Assume we have one feature left and some number of examples in a leaf that does not have separated data points then, either: ...


3

The branching factor is important, as it limits the effectiveness of search. However, the branching factor in chess is already too high to effectively search without techniques that reduce the size of the search space. Even with millions of tests per second, a computer can only check a small fraction of the possible future games in order to find results in ...


2

I don't think that is possible with a decision tree, unless there is some measure of confidence that you can use as a threshold. I ran into the same problem with the ID3 algorithm. It assigns classes, but you only have the resulting class without any confidence or probability attached. One possible solution could be to add a number of counter examples as a ...


2

Please, take a look at Understanding Shannon's Entropy metric for Information. The answer for the minus sign is in section 6. The probability logs are less than or equal to $0$, so the minus sign guarantees that information (entropy) is always greater than or equal to $0$.


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The extraction of features from data and categorizing media are not business decision making. However, determining what patterns in real world information are distinctive and placing an item in one bin rather than another are of a simple kind of decision. That large amounts of data are required to perform these tasks does not discount these limited decision ...


2

You don't need to re-train on the fly. What you're looking for is an embedded feature selection algorithm, and even more precisely, one that minimizes the number of responses required. I think this might be one of the rare cases where genetic and evolutionary approaches are the obviously correct choice. Genetic Programming is a technique for finding models ...


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If I had to implement a path exploration/finding algorithm on a robot, I would follow these steps: Make sure you can detect your position. You need to be able to record your position otherwise you have no reference for the exploration. You don't need a global positioning system (like GPS), a local one is more than enough in your case. This means that the ...


2

All of the statistical learning is about inductive learning. What is the difference between inductive learning and connectionist learning? Inductive learning is about identifying patterns from examples. It is more related to statistics. Connectionist learning is more about finding a common pattern and predicting as well as self-learning(learning from the ...


2

there's a lot to un-pack in this question. Why do they only pick 500 rows? my guess: in order to keep the example running quickly. tsfresh usually takes a while to calculate its features. note that when they evaluated their model, they took the last 500 samples. What's the point of re-arranging the rows/columns? answer: the data frame format that ...


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Consider a dataset $S \in \mathbb{R}^{N \times (M + 1)}$ with $N$ observations (or examples), where each observation $S_i \in \mathbb{R}^{M + 1}$ is composed of $M$ elements, one value for each of the $M$ features (or independent variables), $f_1, \dots f_M$, and the corresponding target value $t_i$. A decision tree algorithm (DTA), such as the ID3 ...


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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 ...


2

For a binary split, there are only three possible operations (or arguably only two if you consider one-hot encoding). Any other kind of split would simply not be binary. Almost every tree-based model is restricted to binary splits, due to the combinatorial explosion when considering ternary or even more complex splits. Of course you could write your own ...


2

There are a variety of conditions we can use when deciding whether to prune a sub-tree or not after generating a decision tree model. There are three common approaches. We can prune branches with less support than a specific threshold. These are branches which were constructed using very few points from the training data. We can prune branches where the ...


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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 ...


1

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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Before the AI itself can be improved, there is a need to draw a clear line between the game and the AI which is playing the game. Both things have nothing in common, there are separate modules in the project. The game rules are defining which movements are possible on the board. They have to be fixed. It can either be a game of pacman, a game of monster ...


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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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A possible application of Decision making systems are interactive computing, knowledge based tutorials and game-based learning. For example, it is possible to model the workflow in a hospital. The expert system has to support the decisions of the human operator. Before the software can explain “good decision”, the domain specific knowledge of the hospital ...


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