Binary logistic regression modeling with TensorFlow™

Logistic regression model is one of the most widely used modeling techniques in clinical medicine, owing to the widely available statistical packages for its implementation, and the ease of interpretation. However, logistic model training requires strict assumptions (such as additive and linearity) to be met and these assumptions may not hold true in real world. Thus, clinical investigators need to master some advanced model training methods that can predict more accurately. TensorFlow™ is a popular tool in training machine learning models such as supervised, unsupervised and reinforcement learning methods. Thus, it is important to learn TensorFlow™ in the era of big data. Since most clinical investigators are familiar with the logistic regression model, this article provides a step-by-step tutorial on how to train a logistic regression model in TensorFlow™, with the primary purpose to illustrate how the TensorFlow™ works. We first need to construct a graph with tensors and operations, then the graph is run in a session. Finally, we display the graph and summary statistics in the TensorBoard, which shows the changes of the accuracy and loss value across the training iterations.