Training a logistic regression model involves teaching it to predict a binary outcome—typically labeled as 0 or 1—based on input features. It’s a foundational algorithm in machine learning, especially useful for classification tasks where the goal is to determine which of two classes a data point belongs to.
The process begins with preparing the data. Each observation in the dataset includes one or more input features and a target label. Before training the model, it’s important to ensure the data is clean and in numerical form, as logistic regression relies on mathematical operations that can’t process non-numeric values. Often, this involves normalizing or scaling the features and converting categorical variables into numerical form using techniques like one-hot encoding.
Once the data is ready, the logistic regression model starts by assuming a linear relationship between the input features and the log-odds of the output. The model computes a weighted sum of the input features and passes the result through a sigmoid function. This function compresses the output into a value between 0 and 1, representing the estimated probability that the input belongs to the positive class.
To make this model accurate, a training process adjusts the feature weights (coefficients) so that the predicted probabilities align as closely as possible with the actual labels in the training data. This is done using an optimization technique called gradient descent. The algorithm calculates the difference between the predicted and true labels—referred to as the loss—and then updates the weights in the direction that reduces this loss.
As training progresses over many iterations, the model gradually learns the most appropriate set of weights. The learning rate determines how quickly or slowly the weights are updated during training, and it must be chosen carefully to ensure the model converges to a good solution without overshooting.
After training, the model can make predictions by applying the learned weights to new, unseen input data. A probability threshold—commonly 0.5—is used to decide whether a prediction should be classified as class 0 or class 1. The model’s performance can be evaluated using metrics such as accuracy, precision, recall, or the area under the ROC curve, depending on the context of the problem.
Logistic regression is favored for its simplicity, interpretability, and efficiency. Despite being a linear model, it performs surprisingly well on many real-world classification tasks, particularly when the relationship between the inputs and the output is approximately linear in nature.