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One common approach for this type of data is take the integral and learn either a translation function to fish weight. Taking the integral simplifies the problem to a single number. You probably do not need a state of the art model. A general linear model would probably pick out a signal.


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After you have finished with the model building process (in which it is assumed that you have used your test set once and only once for assessing the performance of your final model on unseen data), and before deploying your model, both common sense and standard practice say that you should re-train it on all the available data, including the portion that, ...


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As soon as you train with data from a test set it is no-longer a testing set. What you are suggesting would lead to you flying blind: it's possible that you will have better results because you're using more data but you simply would have no way of knowing. This is not a recommended strategy. An alternative would be to change the train/test split to say 90/...


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It's not really possible to adress concept drift in general. But I can bring two similar answers for drift of houses prices : As other prices the drift is usually well measured and studied. As one would correct price for inflation, one can correct past house prices with a housing index (typically this index for the US). It will help your model having prices ...


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High variance means that your model's performance varies from data to data, which is bad for the model. For example: You used a polynomial classifier with high degree, so it will overfit your training data resulting in good accuracy, but when tried on a different dataset it will yield less accuracy , resulting in variance. The image on the right is way too ...


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Let's say you want to learn a specific mapping from $\mathbb{R}^{n}$ to $\mathbb{R}^{m}$. The following elaboration assumes that you are referring to supervised learning. Hyperplane Hyperplanes play a key role in neural networks. Consider the set $H_{v,d} := \{x \in \mathbb{R}^{n} \mid \langle x,v \rangle = d \} $ for $v \in \mathbb{R}^{n}$ and $d \in \...


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