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talhanai avatar talhanai commented on August 25, 2024
  1. You should perform pearson correlation between the features and the outcome (on the training set). Any feature that is less than some threshold for the correlation coefficient (|ρ| < 1e-01) and above some statistical significance (p-val > 1e-02), drop it.
  2. You should take the features you kept and train an L1 regularized model (over the training data). Any features with model coefficients that are almost zero (|β| < 1e-04) drop them.

You can adjust the thresholds as you like.

I hope that clarifies it.

from redbud-tree-depression.

clintonlau avatar clintonlau commented on August 25, 2024

Can I ask what the parsing process was to get the 8,050 examples? Using the transcripts from the training set, I am counting the number of times 'Participant' appears as the speaker while counting consecutive 'Participant' turns as just one example (since they're essentially one response to a question), but this only yields me around 6200 examples. Any advice would be much appreciated!

from redbud-tree-depression.

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