Python: Deeper Insights into Machine Learning

Logistic regression

With our least squares model, we have applied it to solve the minimization problem. We can also use a variation of this idea to solve classification problems. Consider what happens when we apply linear regression to a classification problem. Let's take the simple case of binary classification with one feature. We can plot our feature on the x axis against the class labels on the y axis. Our feature variable is continuous, but our target variable on the y axis is discrete. For binary classification, we usually represent a 0 for the negative class, and a 1 for the positive class. We construct a regression line through the data and use a threshold on the y axis to estimate the decision boundary. Here we use a threshold of 0.5. ...