Logistic Regression¶. Let's set some setting for this Jupyter Notebook. In  import numpy as np import pandas as pd import pymc3 as pm import seaborn as sns import matplotlib.pyplot as plt Step 6: Use the model for prediction¶. In : y_probs = model.predict_proba(X_test, cats_test).Aug 14, 2019 · Luckily, because at its heart logistic regression in a linear model based on Bayes’ Theorem, it is very easy to update our prior probabilities after we have trained the model. As a quick refresher, recall that if we want to predict whether an observation of data D belongs to a class, H, we can transform Bayes' Theorem into the log odds of an ... Jan 10, 2020 · In logistic regression, we want to model the probability of an event given a number of factors (or features/covariates) so that we can predict a binary outcome. (If we have more than two possible outcomes, we would use multinomial logistic regression , also sometimes referred to as softmax regression.) Predicted probabilities using linear regression results in flawed logic whereas predicted values from logistic regression will always lie between 0 and 1. To avoid the inadequacies of the linear model fit on a binary response, we must model the probability of our response using a function that gives outputs between 0 and 1 for all values of \(X\) .