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MXK's user avatar
MXK
  • Member for 4 years, 2 months
  • Last seen more than 2 years ago
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Binary Classification with Imbalanced Target
so I guess even SMOTE won't be a good option then.
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Binary Classification with Imbalanced Target
@Erwan I was thinking about using SMOTE (SMOTE is an oversampling technique where the synthetic samples are generated for the minority class), and the generated data of course will have True for the target feature then I change it to False and aggregate them, hence I'll have a balanced target.
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What are the differences between MLP and DNN?
what do they mean by "DNN can have loops" ??
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What are the differences between MLP and DNN?
Another thing is, on SKLEARN, you can call and use MLP as simple as using a logistic regression, and it's called MLP indeed but you cannot create a DNN from it, but with other frameworks like Tensorflow and Pytorch you have the liberty to create a DNN from MLP's (one hidden layer). So i wanted to get to the bottom of this controversy.
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What are the differences between MLP and DNN?
I have read that this confusion is linked to old literature, but some people argue that the reason might the number of layers, or if they're fully connected or not, and the complexity of the models. Moreover on Kaggle people are having the liberty to use them interchangeably in different contexts.
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Tools for a data science job
this is just an example and not specific to any field, but it comes from a personel experience: - scikit-learn for Classical Machine Learning. - Tensorflox for Deep Learning. - RL-Glue for reinforcement-learning. - Power-BI for reporting and visualization. - SQL Server Integration Services for ETL. - SQL Server Analysis Services for OLAP cubes. - Apache Spark for a large data processing. - MLFlow for your end-to-end machine learning lifecycle. - Cloud services provider for hosting your project
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Machine Learning validation data returns 100% accuracy
I have assumed the same thing, but then when I answered "100% correct answers on the validation set is the best possible outcome", in the correction they have said "This is not correct because the 100% accuracy is an indicator of an overfit model. It may mean your validation data has gotten mixed in with your training data." Anyway, I was trying to learn the right thinking process about these kind of situations. I'm going to accept your answer, and thank you.
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Machine Learning validation data returns 100% accuracy
Thank you @Oliver Foster for your remarks, I share the same exact thoughts as you, but this was one of the questions that I have found during the preparation for GCP Data Engineer old exams, and there was no data given, I know the answer to the quiz question but i thought there might be another thinking process that make me confident pointing out the right option.
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Train and Test Data Split for a Tweet Classification Task
you need to have the same number of features, otherwise, it won't be logical to do so.
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