Comments (2)
- 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.
- 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.
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.
Related Issues (10)
- Feature processing HOT 1
- Question about X_{train, dev} shape and content
- How to calculate the mae, rmse metric? HOT 2
- Some problem for accuracy HOT 5
- Python environment version HOT 1
- No such file or directory: 'data/audio/X_train.npy' HOT 2
- data/audio/x_train.npy
- How to get 8050 training examples (subject responses)? HOT 8
- Question about validation and test data HOT 4
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