Have you watched the relevant lessons? Have a look at lessons 4-6 and look at the notebooks and spreadsheets, and then let us know any specific questions you have based on that material.
@jeremy AND janardhanp22,
You have had very interesting conversation.
"I experimented normal machine learning tasks like random forest and getting f1 score (Train)= 0.8602 and fi (Test) = 8138 for the same dataset."
well, with Fandom Forest, I get F1=0.967 for my data (another dataset similar to tursun_deep_p6.csv).I used RF in Orange.
For my data @janardhanp22 said âsince it sounds like you have structured data. In general, deep learning isnât the best tool for structured dataâ
Did I find contadict here:
place one:
here, @Jeremy said âdo you have structured data. In general, deep learning isnât the best tool for structured dataâ
place two:
@sibnick in this webpage:
said this to my dataset:
âCNN, RNN can be effectively used when parameters have structure (e.g. time series, geometric distribution like pixels in image)â
my data âtursun_deep_p6.csvâ in this webpage:
I believe Jeremy means structured data like user profile or something like this. CNN, RNN work with correlated data - e.g. pixelâs are correlated with neighbor pixels on real images.
Switch Softmax to Sigmoid
@jeremy (or anyone else familiar with the topic) â why does DL seem to struggle with structured data compared to other methods (like rfâs)? is there any interesting published research on the topic? I work on the analytics team for an ecommerce company, so I can think of many interesting problems that use structured data. Iâd love to find a way to get strong performance using neural networks to minimize the âguessworkâ around feature engineering.
From my point of view it is nature of neural networks. They are universal approximators for continuous functions. But if you have no continuous function for initial model or for loss function then NN are not strong.