I’d like to use Julia for a small ML project. My dataset is about 2000 samples and 142 features.
I was hoping to use a simple Neural Network to do this task (with a ReLU neuron as the output node?). However, I just don’t know which package I should use for that! There are a lot of available package, but they seem to be aimed at complex problems/solutions.
Thanks for any suggestions and help! Bear in mind that I’m a total novice in this field. Hence, I’m open to alternatives, but I’m looking for a fairly simple approach to begin with.
To my knowledge, deep learning is state-of-the-art on problems where the spatial/time structure can be exploited (via convolution/recursion), but it hasn’t made great strides elsewhere. With 2000 samples X 142 features, I would consider other classical (regularized) ML algorithms, like random forests or gaussian processes; they are much simpler to setup. Do you have a specific reason to favour deep learning?
No deep reason to use deep learning. I was more looking at a simpler NN. I just thought that having so many features that a NN would be well suited for modelizing the non-linear relationships.
The use case is I want to predict the number of ticket sold at a ski stations, based on these features.
Logistic regression is the simplest example of a neural network model, so I just linked to it for demonstration purposes. TensorFlow is for general deep learning.
I might be biased, but that sounds like a typical use case for scikit-learn-like modelling, whether directly through PyCall, or through ScikitLearn.jl. See eg. the Decision Tree example (could also use a random forest with nearly the same code), or the Gaussian processes example. PCA might help, too.
If you try deep learning, watch out for overfitting.
Hence, I’m open to alternatives, but I’m looking for a fairly simple approach to begin with.