Labeling classes conditionally The 2019 Stack Overflow Developer Survey Results Are InUnderlying model for prediction using different prediction variablesUnsupervised Classification for documentsLSTM for capturing multiple patternsTime-based over-sampling dilemmaDetect rapid increase in time seriesTime series forecasting with RNN(stateful LSTM) produces constant valueswhat is the best approach to my prediction problemLSTM Model for predicting the minutely seasonal data of the dayAudio classification data balanceReinforcement Learning on real time data over a web server
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Labeling classes conditionally
The 2019 Stack Overflow Developer Survey Results Are InUnderlying model for prediction using different prediction variablesUnsupervised Classification for documentsLSTM for capturing multiple patternsTime-based over-sampling dilemmaDetect rapid increase in time seriesTime series forecasting with RNN(stateful LSTM) produces constant valueswhat is the best approach to my prediction problemLSTM Model for predicting the minutely seasonal data of the dayAudio classification data balanceReinforcement Learning on real time data over a web server
$begingroup$
I am working with a time series predicting whether web traffic will increase or decrease each day compared to the previous day for a given user.
Initially I used binary classes: labeled 1 for next day traffic increases and 0 for traffic decreases (which distributed into 60/40 split). Next I tried something conditionally: if the user has increased traffic for 3 previous days in a row and they increase tomorrow, that is labeled 1, else 0. Otherwise, if the user has decreased traffic the previous 3 days and decreases traffic tomorrow, that is labeled 1, else 0. So 1 doesn't always/necessarily correspond to traffic increases, it depends on the condition which can easily be observed when using this algorithm for real life predictions (by simply looking at the data).
With this 'conditional' dependent label encoding I have gotten much better results. The new binary classes are split 55/45 and accuracy and f1 have greatly improved for testing and training sets.
Is this kind of class labeling acceptable and/or good practice? I think it is positive as I am introducing more data without increasing dimensions but I am worried about mixing up the classes with this approach.
Thank you for your help!
classification scikit-learn time-series supervised-learning
asked 16 mins ago
Joe ThomsonJoe Thomson
1
New contributor
Joe Thomson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
$begingroup$
I am working with a time series predicting whether web traffic will increase or decrease each day compared to the previous day for a given user.
Initially I used binary classes: labeled 1 for next day traffic increases and 0 for traffic decreases (which distributed into 60/40 split). Next I tried something conditionally: if the user has increased traffic for 3 previous days in a row and they increase tomorrow, that is labeled 1, else 0. Otherwise, if the user has decreased traffic the previous 3 days and decreases traffic tomorrow, that is labeled 1, else 0. So 1 doesn't always/necessarily correspond to traffic increases, it depends on the condition which can easily be observed when using this algorithm for real life predictions (by simply looking at the data).
With this 'conditional' dependent label encoding I have gotten much better results. The new binary classes are split 55/45 and accuracy and f1 have greatly improved for testing and training sets.
Is this kind of class labeling acceptable and/or good practice? I think it is positive as I am introducing more data without increasing dimensions but I am worried about mixing up the classes with this approach.
Thank you for your help!
classification scikit-learn time-series supervised-learning
asked 16 mins ago
Joe ThomsonJoe Thomson
1
New contributor
Joe Thomson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am working with a time series predicting whether web traffic will increase or decrease each day compared to the previous day for a given user.
Initially I used binary classes: labeled 1 for next day traffic increases and 0 for traffic decreases (which distributed into 60/40 split). Next I tried something conditionally: if the user has increased traffic for 3 previous days in a row and they increase tomorrow, that is labeled 1, else 0. Otherwise, if the user has decreased traffic the previous 3 days and decreases traffic tomorrow, that is labeled 1, else 0. So 1 doesn't always/necessarily correspond to traffic increases, it depends on the condition which can easily be observed when using this algorithm for real life predictions (by simply looking at the data).
With this 'conditional' dependent label encoding I have gotten much better results. The new binary classes are split 55/45 and accuracy and f1 have greatly improved for testing and training sets.
Is this kind of class labeling acceptable and/or good practice? I think it is positive as I am introducing more data without increasing dimensions but I am worried about mixing up the classes with this approach.
Thank you for your help!
classification scikit-learn time-series supervised-learning
asked 16 mins ago
Joe ThomsonJoe Thomson
1
New contributor
Joe Thomson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am working with a time series predicting whether web traffic will increase or decrease each day compared to the previous day for a given user.
Initially I used binary classes: labeled 1 for next day traffic increases and 0 for traffic decreases (which distributed into 60/40 split). Next I tried something conditionally: if the user has increased traffic for 3 previous days in a row and they increase tomorrow, that is labeled 1, else 0. Otherwise, if the user has decreased traffic the previous 3 days and decreases traffic tomorrow, that is labeled 1, else 0. So 1 doesn't always/necessarily correspond to traffic increases, it depends on the condition which can easily be observed when using this algorithm for real life predictions (by simply looking at the data).
With this 'conditional' dependent label encoding I have gotten much better results. The new binary classes are split 55/45 and accuracy and f1 have greatly improved for testing and training sets.
Is this kind of class labeling acceptable and/or good practice? I think it is positive as I am introducing more data without increasing dimensions but I am worried about mixing up the classes with this approach.
Thank you for your help!
classification scikit-learn time-series supervised-learning
classification scikit-learn time-series supervised-learning
asked 16 mins ago
Joe ThomsonJoe Thomson
1
New contributor
Joe Thomson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 16 mins ago
Joe ThomsonJoe Thomson
1
New contributor
Joe Thomson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 16 mins ago
Joe ThomsonJoe Thomson
1
New contributor
Joe Thomson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 16 mins ago
Joe ThomsonJoe Thomson
1
asked 16 mins ago
Joe ThomsonJoe Thomson
1
1
New contributor
Joe Thomson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Joe Thomson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Joe Thomson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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