# Dealing with small sample size

The objective is to classify a variable Y which is binary, outcomes [0,1], where all the features $$X_1$$....$$X_{156}$$ are normalized and continuous.

The methods I'm using are Logistic regression and XGBoost, I'm rather new when it come to this method.

The result after using feature selection gives an AUC a bit under $$70\%$$. The reason I think that the result is rather poor might be because of the data.

So I've got data with the following dimension:

156 features and 625 data points. It would seem that the sample size is too small in relation to the amount of features.

Thus I wonder if a resampling method would be adequate such as bootstrap?

• What's the objective and data type? – user2974951 Oct 15 '18 at 11:59
• Your question is rather unclear. What are you trying to do? My blog post how to ask a statistics question may help you formulate your question in a way that can be answered. – Peter Flom - Reinstate Monica Oct 15 '18 at 11:59
• edited the question, added some more details – Erik Castillo Oct 15 '18 at 12:53
• As yourself this question. You go to the store and at the checkout counter the bill comes to \$20. You have$10. Would the clerk be willing to magnify the \$10 to equal \$20? You can't create data, or money. – Frank Harrell Oct 15 '18 at 12:58

• Think of bootstrapping a financial time-series signal. If you apply bootstrapping directly, how can it make a reasonable sequence? New samples of stock closing prices could have 2000$at time t, 4400$ at t-1, 300\$ at t-2. Time-series signals are not i.i.d or randomly sampled. Each sample is correlated with its past or future values. But I say that, it may be possible to bootstrap a time-series signal with a clever approach(integration of a ARIMA model maybe), just not the direct way. I do not know it, if you know such a method, please explain, that would be really good to know, sincerely. – Ugur MULUK Oct 15 '18 at 14:31