Roadmap.md

Roadmap

Laying out feature enhancements to add.

Features:

Inputs:

• scaled inputs using other preprocessing scaler

• redundant (correlated and dependent - say by a linear combo of informative features)

• separation between classes (can we filter +/- k% on either side of p_thresh to create separation?)

• overlap - since we have ground truth probabilities, we could sample from a binomial distribution with probability of (py|x) to determine labels - this would work in conjuction with sig_k which controls the steepness of the sigmoid

• noise level - apply during generation of regression values/labels

  • sample coefficients of symbolic expression from std normal distribution

• outlier generation

• create fake PII with pydbgen (stretch, new)

Output:

• mapping from y_reg value to y_class
  • partition and label - e.g. y_class = y_reg < np.median(y_reg)
  • Gompertz curve (a parameterized sigmoid - would give control over uncertainty? -[ ] noise (e.g. flip_y) -[ ] map class to probability using random draw from binomial distribution

Capability/Scaling:

• test scaling complexity based on number of features
• research alternatives to multivariate normal copula