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I'm afraid your data is probably too complex and specific for somebody else to understand exactly what's going on. The only idea I can suggest is to try to analyze manually the errors that your model makes: Are there any patterns, like a kind of errors which happens quite often? For instance a country which tends to be overestimated, a month which tends ...


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There is several techniques that could work for you: Target Encoder: Works well when there is a high cardinality of a categorical feature. Ordinal/Label Encoding: Tradition label encoding Weight Of Evidence: tells the predictive power of an independent variable in relation to the dependent variable


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That's not how it works. There is a given number of weights given by the architecture you chose. Those weights are then updated on each iteration of the learning process. Depending on your method of learning, your weights may be updated once on 1000 observations, or for groups of observations (even groups of 1). May I suggest you to read an introduction to ...


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It's hard in general, because you don't know the joint distribution of the data, let alone joint distribution with the label -- or else you wouldn't need a classifier. Without that you can't confidently sample new, valid instances. In the image case it's 'easy' because we know certainly that a rotation or shear or scale of an image produces another valid ...


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Whether this particular performance is stable is mostly a matter of opinion, and it depends on the context as well (size and complexity of the data). It's often more meaningful to use a baseline system and compare the performance against the tested system: this way it can objectively be said whether the performance is more or less stable than the baseline.


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I don't have enough reputation to comment though, but how many features does your data have? How about using Lasso Regression to select features (using 50 anomaly data and another random 50/100 normal data, assuming that normal data come from the same distribution) and then see when plotting normal vs. abnormal data points, are abnormal points separated ...


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There is an attribute called as score you can use it as model.predict_score(X) which returns anomaly score based on this you can classify it as inlier or outlier.


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I am not sure if I understand your question correctly, but in your model, you are tuning your "alpha" parameter, you have a range from 1 to 0. (1 -> 0.1 -> 0.01 -> 0.001 -> 0.0001 -> 0). The grid search will evaluate each algorithm (SVD, CHOLESKY,...) with each possible value of your "alpha" parameter. It will define the score for each alpha parameter (eg. ...


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Scikit uses numpy for pseudo-random number generation. So to fix random state in various scikit calls, you use numpy.random.seed(12345) and then use scikit. You would want to record the random seed when you log the model so you could reproduce the same run later. If your code (or something you call) also uses Python's random number generator, you would set ...


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Lasso stands for ´least absolute shrinkage and selection operator´. It has a penalty that is the absolute value and makes a lot of variables converge to cero. There is a ton of blogs that explain really well Lasso on the internet, have a look! Elastic Net is a combination of Ridge and Lasso. So it will also reduce the variables a lot. Ridge is a quadratic ...


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you can develop simple predictive model like Linear regression to predict price of house given other features value, also analyse the features weight/coefficient and optimize your linear regression model.


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1) It seems that your data are unbalanced, you should look into that. Common techniques include oversampling the minority class, but you might have a bigger problem here. 2) It is unclear that you have enough information for what you are trying to achieve (type of device and location doesn't seems to be enough). 3) Based on the two preceding remarks, you ...


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