Introduction to Probabilistic Classification: A Machine Learning Perspective

<p>You are capable of training and evaluating classification models, both linear and non-linear model structures. Well done! Now, you want class probabilities instead of class labels. Read no more. This is the article you are looking for. This article walks you through the different evaluation metrics, its pros and cons and optimal model training for multiple ML models.</p> <h1>Classifying cats and dogs</h1> <p>Imagine creating a model with the sole purpose of classifying cats and dogs. The classification model will not be perfect and therefore wrongly classify certain observations. Some cats will be classified as dogs and vice versa. That’s life. In this example, the model classifies 100 cats and dogs. The <a href="https://en.wikipedia.org/wiki/Confusion_matrix" rel="noopener ugc nofollow" target="_blank">confusion matrix</a> is a commonly used visualization tool to show prediction accuracy and Figure 1 shows the confusion matrix for this example.</p> <p><a href="https://towardsdatascience.com/introduction-to-probabilistic-classification-a-machine-learning-perspective-b4776b469453"><strong>Visit Now</strong></a></p>