Confusion Matrix

A detailed Guide with example on confusion matrix and metrics used in them and tradeoff between them. Classification in Machine learning: Predicting a discrete category if we provide input is termed “classification” in machine learning. Example: Predicting the weather that it will rain today or not in a city (two categories, so binary classification). Predicting…

What is an acceptance sampling plan ? Originated in ww2 by Dodge and Romig to test bullets, a decision making process by sampling inspection results to accept or reject a receiving lot so called Lot acceptance sampling or simply acceptance sampling. Now widely used in quality control worldwide environment. 6.2.1. What is Acceptance Sampling? Contributions of Dodge and Romig to acceptance sampling Acceptance sampling is an important field of statistical…www.itl.nist.gov

As machine learning practitioners, we tend to use accuracy to evaluate the model that we create. But did you know what the concept behind it is? In this article, I would like to share the secret behind it. I will try to write in very easy language so everyone with…

The confusion matrix provides more insight into not only the performance of a predictive model, but also which classes are being predicted correctly, which incorrectly, and what type of errors are being made. The precision and recall metrics are defined in terms of the cells in the confusion matrix, specifically…

In my first post I would like to spend some time going over a fundamental topic that often confuses beginners. How do we assess the performance of our model when presented with a binary classification problem such as trying to identify malignant tumours or fraudulent transactions? …

What is Confusion Matrix? A confusion matrix is a performance measurement (often represented in the form of a matrix) technique for Machine learning classification models. A confusion matrix is a two-dimensional matrix with two rows and two columns where rows are represented with the predicted values and columns with actual values.

In Machine Learning, Once we have the result of the Classification Problem. “There is a confusion to find the accuracy of the model, which model is accurate for our problem and how to find which model is best ? ”. For Regression Problem: R-squared, MeanSquearedError matricin a regression problem, Basically…

This article was originally posted on our company website. Flexday Solutions LLC is a team of thought leaders with a mission in delivering advisory and implementation services in AI, ML and cloud solutions from Illinois, USA. Recently we worked on several projects where we used large sets of tabular data…

In classification problems, accuracy of a model can be the main performance indicator in many cases. But there are some cases accuracy is not enough to evaluate the performance of a model. When the data you are working with is unbalanced, accuracy score can be misguiding. …