From the course: Microsoft Azure AI Essentials: Workloads and Machine Learning on Azure

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Binary classification

Binary classification

From the course: Microsoft Azure AI Essentials: Workloads and Machine Learning on Azure

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Binary classification

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- [Instructor] Binary classification predicts one of two outcomes, like yes and no or valuable and not valuable. It's a supervised technique requiring features to have assigned labels. Like regression, it follows an alternative process of training, validating, and evaluating. However, classification algorithms calculate probabilities for class assignment, not numeric values. For example, let's build a model to predict if a person will develop diabetes based on features like blood pressure, cholesterol, BMI, and smoking habits. We trained the model using an algorithm that fits the data to a function, calculating the probability of diabetes ranging from 0-1. For instance, if the probability is 0.7, then the chance of not having diabetes is 0.3. Similar to regression, there are many algorithms: logistic regression, decision tree, random forest, and support vector machines among others. Logistic regression is popular for its simplicity. Using a sigmoid s-shaped function ranging from 0-1…

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