x
import numpyfrom sklearn import linear_modelX = numpy.array([3.78, 2.44, 2.09, 0.14, 1.72, 1.65, 4.92, 4.37, 4.96, 4.52, 3.69, 5.88]).reshape(-1,1)y = numpy.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])logr = linear_model.LogisticRegression()logr.fit(X,y)def logit2prob(logr, X): log_odds = logr.coef_ * X + logr.intercept_ odds = numpy.exp(log_odds) probability = odds / (1 + odds) return(probability)print(logit2prob(logr, X))[[0.60749955] [0.19268876] [0.12775886] [0.00955221] [0.08038616] [0.07345637] [0.88362743] [0.77901378] [0.88924409] [0.81293497] [0.57719129] [0.96664243]]