Confusion Matrix

Introduction Machine learning has revolutionized the field of data analysis, enabling us to make accurate predictions and classifications. Logistic regression is a popular and fundamental classification algorithm. In this article, I will delve into the intricacies of logistic regression, exploring its principles, applications, and implementation using a Scikit-Learn dataset. Logistic regression…

Previous topics can be found here part 1:Create an Azure ML workspace:https://medium.com/@aneesha161994/azure-dp-100-exam-part-1-d830b32db1ff part 2:Manage data objects in an Azure ML workspace:https://medium.com/@aneesha161994/azure-dp-100-part-2-manage-data-objects-in-an-azure-ml-workspace-3a0bac0eef70 part 3:Data Transformation Techniques in Azure ML:https://medium.com/@aneesha161994/azure-dp100-part-3-data-transformation-techniques-9d6aaa3897cf

“You’re the average of the five people you spend most of the most time with” ~Rim John Peers and friends play a crucial role in the process of socialization, behavioural metamorphosis and moulding an individual’s perceptions of the outer world and decision-making. Their influence and pressure have played a pivotal…

How to intuitively explore model predictions on specific images over time — Introduction We often rely on scalar metrics and static plots to describe and evaluate machine learning models, but these methods rarely capture the full story. …

This guide explains what a Confusion Matrix is and how it is used to measure the performance of classification models. — It includes the metrics: Sensitivity and Specificity, True Positives, True Negatives, False Positives and False Negatives. — What is a Confusion Matrix? A confusion matrix is a table that summarizes what a classification model classified right and what it classified wrong during a test.

The evaluation metrics for classification models provide a quantitative assessment of model performance. The choice of evaluation metric depends on the specific problem and the importance of false positives and false negatives. It is essential to carefully select the appropriate metric(s) for evaluating the performance of a classification model. Confusion matrix The…

How to evaluate a model The classification matrix is a table that shows the summary of classifications of a dataset. It helps us understand what the model is getting right and the number of errors it produces. Sensitivity / Recall / True Positive Rate = TP / Actual Positive = TP / (TP + FN) Model’s ability…

Index What is confusion Matrix? Different Metrics derived from Confusion Matrix Connection with Type I and Type II errors in detail Power of test (1 — β) Use Cases Case Study List of Interview Questions What is Confusion Matrix? A confusion matrix is a table that is often used to describe the performance of a…

A confusion matrix is like a chart that helps us see how well the machine learning model is doing. Imagine we have a list of things that we want the machine to recognize, like pictures of cats and dogs. …