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For every 15-25 samples, you can have 1 additional feature. In rarest cases it might be low as 10 samples for each feature, however, it is not recommended unless you have good knowledge of your data and can safely be sure or test that your model is not showing spurious results. For more, please read the source. Using a few samples or breaking above mentioned ...


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Logistic regression is a popular probabilistic prediction algorithm that outputs a probability (from 0 to 1) in contrast to a regression problem that could output any value. The "generic dataset" its training on should be labeled 0 or 1 before training the algorithm. It is recommended for "tabular" datasets with rows of each sample and ...


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The update rules are ; $a := a - \alpha.\frac{\partial L}{\partial a}$ $b := b - \alpha.\frac{\partial L}{\partial b}$ Here L which is the Loss [Squared Error in this case ] is given by; $L = \frac{1}{2}.(\hat{y} - \frac{1.0}{(1.0 + exp^{-ax + b})})^{2}$ So ; $\frac{\partial L}{\partial a} = \frac{1}{2}.2(\hat{y} - \frac{1.0}{(1.0 + exp^{-ax + b})}).\frac{-...


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One of the main advantages of tree-based methods is that "high correlation" is not so much of a problem (when compared to linear models). Another advantage is that there is no parametric assumtion behind the model. Thus you have a good chance that some of the variables you dropped to do the Logit (high correlation/u-shaped) can be used with ...


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Interesting problem which potentially involves many aspects of ML, here are a few thoughts: At first sight I thought that this looks more like an optimization problem, not a regular classification problem. In this case I would suggest to maybe try things like genetic learning, because it can find an optimal assignment for individual elements which maximizes ...


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Like @lcrmorin said, this is not a lot of data points. For some problems and algorithms, it is enough data. Some problems are easy to predict and some are hard. Some algorithms (neural nets, gbm) are data hungry and may not do well with this small amount of data. That is the start. Second, whatever metric(s) you are using - you mentioned accuracy but is that ...


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100 data points is a very very low number of points. I am quite surprised that the algo managed to learn something meaningful (well unless you have very obvious relationships between the features and the target). I'll suggest you look at what the logistic regression learned exactly (what coefficients are deemed significant). There is an important risk that ...


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I think your current under-performance is a data problem. The dataset in your google sheet seems like it contains far too few features that you could conceivably use to predict whether someone would win a tennis match. You should consider some creative feature-engineering options. Stuff like (but not limited to): how many wins or loses in a row has this ...


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