New answers tagged data
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If the model is a discriminative model (e.g. a classification model), it is highly unlikely that you can identify whether it was trained with some specific dataset.
If the model is generative, (e.g. a language model or a machine translation system), you may be able to try to identify if the model was trained with your data by trying to extract from it ...
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There are two type of solutions here.
Either you continue working with jupyter notebook. Then you need an approppriate diff/mergetool. I recommend this one (https://github.com/jupyter/nbdime).
Another suggestion is to use frameworks that convert python files into notebooks. By which I mean, they allow you to interact with python files as if they are ...
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In ML you really need good examples and then things for which you don't know the outcome. You learn from the good examples and then apply this "knowledge" to the examples for which you wish to know the outcome.
I agree that Mathematical Optimisation would probably be a better route to take in a problem such as this.
Alternatively, if you want to ...
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I think the question was asked to see how would you approach the problem. In similar questions, there is not a single answer, and the interviewer does not expect a certain answer instead expects a reasonable approach by you. It is like the famous interview question "How many golf balls can you fit into a swimming pool?". Such a question is asked to ...
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If an information is never available for one of the classes, it's not a usable indication. So it seems to me that the login name is simply irrelevant for the task, so it shouldn't be included as a feature.
Mixing different sources of data can be ok in some cases, but only if the different datasets provide consistent features and are generally representative ...
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You're experiencing an unfortunately common issue with the current state of system/model evaluation. In addition to evaluating on different datasets, authors often leave out important details, such as the procedure for hyperparameter tuning, detailed evaluation metrics (i.e. true positives, false negatives, etc. in addition to F-score), and ablation analyses....
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Programming best practices that are specifically usefull for ML / DS :
Know and handle your data types, with a dict for example, and pass this dict as and agrument to your read csv. You can get important memory usage reduction by using the right types of float / int and category instead of string. Float precision is rarely important in ML (and usually ...
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To answer that we Need to have a formal Definition of real intelligence. You got multiple definitions, but which one satisfies you?
Lets say you Claim real intelligence is Awareness. Than if you ask a machine can you prove to me that you are Aware you wont be satisfied. But can you prove you are Aware.
I mean at the end of the day it does not even matter. ...
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Let's say you have very frequent data across a period of time T and you want to sample N points.
Instead of sampling points directly you could sample the time that separate them. You then just need to add 12 hours to all gaps to enforce your constraints.
To do so you could sample N points uniformly in [0, T - (N-1) x 12h] and then compute the difference ...
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Impact of imbalanced datasets
First I would say that imbalanced dataset impact depends on the type of model you are using.
For instance:
Gaussian Naive Bayes should not be that much impacted if you have a certain amount of data for each class that is enough to approximate your gaussian distributions. (and that your data are normally distributed)
Neural ...
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If you are looking for an out of the box solution to deploy your model and get a REST endpoint/web app built for you on the fly, you can have a look at clouderizer. It provides you a clean GUI to drag n drop your packaged model file and deploy it on AWS/GCP/local machine. Here's a web app built for Heart failure prediction using clouderizer.
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From the example I assume that an instance corresponds to a user, and you have both full sequences of the mouse and keyboard as features for predicting the user. I can think of two options for using these features in the same model:
With feature engineering, find a way to represent both sequences as a fixed array of features. For example you might have ...
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