The following article explains how to use TensorFlow Core APIs to create, train, and evaluate a logistic regression model for binary classification of tumors using the Wisconsin Breast Cancer Dataset
To begin, samples are loaded efficiently with the TensorFlow API and the values of relevant features are normalized to improve the model's generalization capability
Next, a simple logistic regression model is defined with a dense layer that uses the sigmoid function to predict the probability of malignancy. Training is performed by minimizing logarithmic loss and optimizing weights using the TensorFlow Core Adam optimizer
Once training is complete, the model is evaluated by calculating metrics such as accuracy, precision, and recall. This allows validating the effectiveness of binary tumor classification and adjusting hyperparameters according to the results obtained
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