Dealing with Unbalanced Data with Minibatches

[tomkov]

I found something that looks like class weighting here:

How to model/deal with weighted binary outcomes

Updated solution after this topic How to perform weighted inference import numpy as np import pymc3 as pm import theano.tensor as T N = 1000 P = 0.2 data = np.zeros(N) data[:int(N * P)] = 1.0 weights = np.ones(N) weights[:int(N * P)] = 4.0 with pm.Model() as model: p_ = pm.Normal('p_', mu=0.0, sd=1000.0) p = pm.Deterministic('p', pm.math.invlogit(p_)) pm.Potential('weights', weights * pm.Bernoulli.dist(p=p).logp(dat…

I’m still testing it.