#KDnuggets #MachineLearning
Detection theory, Statistical classification, Sensitivity and specificity, Machine learning, Statistics, Artificial intelligence
Confusion Matrix, Precision, and Recall Explained
Learn these key machine learning performance metrics to ace data science interviews.
Confusion Matrix, Precision, and Recall Explained
Learn these key machine learning performance metrics to ace data science interviews.
We are sorry, we could not find the related article
If you are curious about Artificial Intelligence and General News
Please click on:
Subscribe to Artificial Intelligence - General News
Everything you Should Know about Confusion Matrix for Machine Learning
What is a Confusion matrix? Why do you need it? And how do you implement it in Scikit-learn? Clear all your confusion in this article!
Making Predictions with Logistic Regression (Scikit-Learn)
In this tutorial, I will be sharing how to make predictions using logistic regression. The purpose of using logistic regression really…
![Confusion Matrix: How To Use It & Interpret Results [Examples]](https://assets-global.website-files.com/5d7b77b063a9066d83e1209c/632080f2a7b0ab99e500879d_Group%205.png)
Confusion Matrix: How To Use It & Interpret Results [Examples]
A confusion matrix is used for evaluating the performance of a machine learning model. Learn how to interpret it to assess your model's accuracy.
Why the high accuracy in classification is not always correct?
The high accuracy of the classification model could be misleading as there could be an imbalance in the predictions.
Clinician checklist for assessing suitability of machine learning applications in healthcare
Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they ...
15 Popular Machine Learning Metrics For Data Scientist
The article was about the popular machine learning metrics. We described fifteen of them here. We hope, this would be very helpful for you.
Automatically generate model evaluation metrics using SageMaker Autopilot Model Quality Reports
Amazon SageMaker Autopilot helps you complete an end-to-end machine learning (ML) workflow by automating the steps of feature engineering, training, tuning, and deploying an ML model for ...
F-Score
The F score, also called the F1 score or F measure, is a measure of a test’s accuracy.
Important Evaluation Metrics for the ML Classifiers
Assessing the performance of a machine learning model is an essential step in a predictive modeling pipeline. Once a model is ready, it has to be evaluated to...
Test Your Machine Learning Model through Model Accuracy
Developers are often coming up with machine learning models for the benefit of the company. Model accuracy tests are performed to check their receiver operating characteristic, ...
Metric Matters, Part 1: Evaluating Classification Models
You have many options when choosing metrics for evaluating your machine learning models. Select the right one for your situation with this guide that considers metrics for classification ...
How to get started with machine learning and AI
We wrap our heads around the basics of AI/ML and show you how to get a model off the ground.
Can laboratory parameters be an alternative to CT and RT-PCR in the diagnosis of COVID-19? A machine learning approach
In this study, a machine learning-based decision support system that uses routine laboratory parameters has been proposed in order to increase the diagnostic success in COVID-19. The main ...
Measuring Model Goodness – Part 1
Building a good and reliable machine learning system requires us to be methodical by: understanding the business needs acquiring and processing the relevant data accurately formulating the ...
Machine Learning Glossary
Compilation of key machine-learning and TensorFlow terms, with beginner-friendly definitions.
14 Popular Machine Learning Evaluation Metrics
In this article, we explore popular metrics for measuring and quantifying the performance of machine learning models.