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Let’s take an example. You are designing an automation script to perform a particular activity. To develop an automation, you first need the data and the well defined set of rules to follow to reach the desired output. In Machine Learning, things ar...
A student has exams after their training is done. So does the model. There are certain algorithms (now here we don’t mean machine learning models) that are used depending on the problem you have trained the machine learning model for. This is an extr...
We have already discussed accuracy as a metric, its limitations and confusion matrix in the previous post in the series. This post will cover the metrics that we can derive from confusion matrix and how they serve as a better alternative than looking...
When it comes to evaluating machine learning models, picking the right metric is like picking the right outfit for an occasion—it can make or break your impression. Sure, accuracy and precision-recall are great, but they sometimes fall short. AUC-ROC...
When it comes to evaluating regression-based machine learning models, picking the right metric is like picking the right seasoning for your dish—the wrong choice could leave a bitter taste. We have already previously covered the metrics which you can...
Preparing your dataset, especially the test set, is a crucial step in building reliable and high-performing machine learning models. Proper dataset preparation ensures that your model is not only accurate but also generalizable to new, unseen data. L...
