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I have a highly imbalanced binary classification problem, probably 95:5 for two classes. I don't want to perform resampling as the data is already huge and training it would just...
Asked on 09/13/2020
1 answerI work at a nonprofit youth center that has 2 distinctive programs throughout the year. Our afterschool program runs during the school year, and our summer camp program runs during...
Asked on 09/13/2020 by Ethan Thompson
1 answerQ1: I have a multivariate time series dataset. For each timestep, there are 11 features and 1 output. I am going to use supervised ML to predect the output....
Asked on 09/11/2020 by jkjsdf fod
1 answerI have this dataset which I really need to use association rules techniques on. The dataset has like 90 variables, many of which are ordinal. Thing is, the data is...
Asked on 09/10/2020 by NeR0
1 answerI'm currently doing a binary classification for sentiment prediction. Currently I have the majority class (~90% of the data) as my positive class (labelled 1) and the minority class (~10%...
Asked on 09/10/2020 by misheekoh
1 answerSuppose I have such a JSON file:[ { "id": "0", "name": "name0", "first_sent": "date0", ...
Asked on 09/09/2020 by martina
1 answerI have a dataset with labels and usernames:Labels Usernames1 Londonderry1 Londoncalling1...
Asked on 09/08/2020 by Math
1 answerI am wondering whether there is any scenario in which gradient descent does not converge to a minimum. I am aware that gradient descent is not always guaranteed to converge...
Asked on 09/08/2020 by wit221
5 answerI designed a custom net architecture (16 layers net) for a binary image classification task and here some highlights:input image: 100x100x3 of two classes(car and person)(normalized and randomized)output layer: sigmoid...
Asked on 09/08/2020 by Osama Khafagy
0 answerI've tried StackOverflow and Comp Sci SE, and everyone points here. I wrote an algorithm for data analysis using the CERN ROOT framework. It takes in three files of sorted...
Asked on 09/08/2020 by Algorithm
0 answerGet help from others!
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