0.318             0.54           0.541
  Belief in Star Sign   7115     5             0.331      0.245             0.32           0.334

  : Prediction Accuracy on Categorical Data[]{data-label="acc_cat"}

Mode is the ratio of the dominant class. Homogeneity is the probability two random samples will be of the same class. The F1-Score is the harmonic mean of precision and recall. For non-binary labels, the precision and recall for each class is weighted by its support.

  **Model**             **Accuracy**
  --------------------- --------------
  Human Majority Vote   0.840
  LIWC                  0.784
  Tri-grams             0.914
  Tri-grams + LIWC      0.916
  BoW (40k Vocab)       0.903
  BoW (500k Vocab)      0.928
  49 layer char-CNN     0.901

  : Gender Prediction[]{data-label="acc_gender"}

Human baseline is the majority vote (n=210) in gender prediction on Twitter data [@nguyen2014gender]. LIWC and Tri-grams are reported in [@schwartz2013personality].

Performance {#accuracies}
-----------

### Gender

Table \[acc\_gender\] compares our gender predictor to several other methods. The BoW model with a vocabulary of 500,000 yields accuracy of 92.8%, 1.4% more accurate than the tri-gram model reported by Schwartz et al [@schwartz2013personality]. Even though the same dataset is used, the comparison is not direct. The tri-gram model seeks to remove the age information from words, has a larger vocabulary, preserves some temporal relationships in the tri-grams, and draws a different train/test split. Moreover, the preprocessing 