New answers tagged data-science-model
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What techniques are used to analyze data drift?
One way to start is fundamental exploratory data analysis.
Compare univariate, bivariate, and multivariate distributions between training data and new data. Those comparisons can be done visually, ...
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What techniques are used to analyze data drift?
It depends about what type of data are we talking: tabular, image, text...
This is part of my PhD, so I am completely biased, I will suggest Explanation Shift. (I would love some feedback). It works ...
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How to forecast a timeseries with geolocation data?
On first impressions, I'm inclined to segregate data for locations which have significance (need not just be home, gym, office), from insignificant ones (travel, traffic) using continuous time spent ...
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How to forecast a timeseries with geolocation data?
You are correct there are many ways to model that data, in particular predicting future latitude and longitude coordinates. It sounds like you want to model the geospatial data probabilistically.
One ...
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How to forecast a timeseries with geolocation data?
I see multiple possibilities, here:
In General
Some general remarks first:
When designing you model, you should take reoccurring patterns into account: There will probably be a 24h pattern (for ...
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99% accuracy in train and 96% in test is too much overfitting?
A significantly higher accuracy on the training set than the test set is generally an indication of overfitting. In your case, the difference in accuracy between the train and test sets is relatively ...
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How to handle missing value if imputation doesnt make sense
If the variable is categorical and not ordered, it may make sense to create a new category 'not_married' to represent the missing values. This would allow you to keep the information about marital ...
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What to do if your model's prediciton result wrong because of unlucky?
All models are wrong, some are useful. The most useful ones are the ones constructed by people who understand the probabilities of things happening, even "black swan" events (things everyone ...
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How to handle missing value if imputation doesnt make sense
I'm not sure of the performance, but if it was me I would also explore categorical variables (One-hot encoding). You could make the features:
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I am getting all scores as 100% on my machine learning models. Is it okay to have this kind of result?
This could indicate one of two things. Either your model is overfitting to the test data, or your features fully explain your target variable. The only way to be sure would be if you had (or could ...
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How to handle missing value if imputation doesnt make sense
Your approach of a binary categorical feature, is_married definitely sounds good.
In some of my projects, I have checked for the percentage of missing values in a column. For instance, if a certain ...
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How to handle missing value if imputation doesnt make sense
You could consider setting years_married to -1, then it is different from columns for the ones that are just married and could thus be understood by a decision tree....
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How to handle missing value if imputation doesnt make sense
I think this is a good solution. You could also try to set a unique negative value for non-married people, especially if you are using a tree-based model.
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