For an assignment we are given a dataset and we need to build a neural network to make new predictions.
Personal milestone; I built my first neural network!
I followed the following steps on my own data set:
The initial set-up of a neural framework is fairly straight forward when following the steps in the tutorial. The next step is to tune the hyper parameters and here is where I get stuck. Important to note that my data set contains 22.000 observations with 160 parameters. There parameters are continuous variables, but also dummy variables.
I learned about the number of layers, the size of a layer, the activation function within a layer and the batch size. There are so many possible combinations and that's where the problem arises. Preferably I do a grid search and let my machine run overnight, so that I can find out which combinations of hyper parameters works the best.
However, then I still need to have a basic idea about the ranges of the parameters and the choices of the activation functions.
What did I do myself?:
I searched for autoML libraries and found the following:
I am considering using autosklearn.classification, but is it advised to do so?
My question is:
- I have 0 intuition about tuning the hyper parameters (this is my first encounter with ML), how do I choose the right hyperparameters?
- It is advised to use the following code?:
import autosklearn.classification cls = autosklearn.classification.AutoSklearnClassifier() cls.fit(X_train, y_train) predictions = cls.predict(X_test)
Any advice would be very much appreciated!