Gradient Boosted Trees vs Neural Network for limited data I have a classification problem, with about 10 different inputs, some boolean, some categorical (and unrelated to each other), some being a float between 0 and 1, which need to be mapped to 4 different outputs.
My problem is that the amount of data that I have is relatively limited. I have about 10,000 data points.
What would make more sense here? Boosted Trees or a Neural Network? I wonder if it makes sense to use a Neural Network at all, given that training an NN seems to require much more data.
Please note I don't want to use SVM, k-means, etc, ideally want to make one of these two methods work. 
Also what parameters would you suggest? Like number of trees/leafs? Number of hidden layers? I know a lot of it boils down to experimentation, but what are good/proven starting values to get good results?
 A: You cannot determine which machine learning algorithm and hyperparameters are ideal until you fit models based on a combination of machine learning algorithms and hyperparameters.  To find out which algorithm is optimal, you'll have to try a few beyond just the two you've listed.
To do this, use a nested cross-validation approach to optimize which combination of hyperparameters to use for each machine learning technique.


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*Split the data into training and testing sets.

*For each machine learning algorithm, determine potential combinations of hyperparameters by examining the specifications of each machine learning algorithm. For each combination of hyperparameters, fit models using the training data and cross-validation; and calculate the mean accuracy.  Choose the model with the hyperparameter combination with the highest mean accuracy.  This will be the optimal model for this machine learning algorithm.

*Compare the accuracy of each optimal model between the machine learning algorithms by testing them using testing sets.


Here are some good references with some examples:


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*Nested Cross Validation by Chris Albon

*Machine Learning FAQ by Sebastian Raska
