Classifier Recall, Explained
Machine learning – and the related field of AI – will probably be worth millions of dollars for people who master these skills. But as I always tell my students:...continue reading.
Category: Machine learning
Classifier Precision, Explained
This blog post explains precision in classifiers and machine learning models. It will explain what precision is, the pros and cons of this metric, how to improve precision, and more....continue reading.
Sklearn confusion_matrix, Explained
Scikit-learn, which is affectionately known as sklearn among Python data scientists, is a Python library that offers a wide range of machine learning tools. Among these tools is the confusion_matrix...continue reading.
Classification Accuracy, Explained
This blog post will explain classification accuracy. It will explain what accuracy is, the pros and cons of this metric, how to improve accuracy, and more. Table of Contents: A...continue reading.
False Negative: What they are, Why They’re Bad, and 7 Ways to Fix Them
When you’re working with classification and detection systems, you’ll commonly hear the term “False Negative.” You might be asking, what is a False Negative? And if you’re a serious machine...continue reading.
False Positive, Explained
Have you ever had someone talk about a classification system or medical diagnostics and mention a “False Positive?” It’s ok … False positives can be confusing if you haven’t worked...continue reading.
True Negative, Explained
If you want to master building classification systems for machine learning, you need to understand how to evaluate classifiers. And in turn, that means you need to understand classification metrics....continue reading.
The Best Way to Plot an ROC Curve in Python
This tutorial will show you how to plot an ROC curve in Python using the Seaborn Objects visualization package. The tutorial is divided into sections for easy navigation, so if...continue reading.
Top 10 Machine Learning Evaluation Metrics for Classification – Implemented in R
So, you’ve trained a classification machine learning model. Now what? How do you evaluate it? That’s where machine learning evaluation metrics for classification come in. This article brings you the...continue reading.
True Positive, Explained
When you begin immersing yourself in the world of classification systems, you’ll encounter a large number of different classification metrics: precision; recall; accuracy; sensitivity and specificity; F1-score; and many more....continue reading.
Scikit Learn roc_curve, Explained
This tutorial will show you how to use the Scikit Learn roc_curve function. It will explain the syntax of the function and show an example of how to use it....continue reading.
Exploring Machine Learning-Derived Data in Life Sciences with Shiny Applications
Machine learning (ML) is gaining widespread popularity in the life sciences. Crafting intuitive user interfaces speeds up data exploration and offers modernized ways to present analyses and outcomes from various...continue reading.
The ROC Curve, Explained
In machine learning, evaluating the performance of a model is as important as its creation. We need tools and techniques to help guarantee that the model performs well and meets...continue reading.
Confusion Matrix, Explained
The confusion matrix is an important and commonly used tool in machine learning. This is particularly true of classification problems, where we build systems that predict categorical values. Because they’re...continue reading.
How to Use the Sklearn Logistic Regression Function
In this tutorial, I’ll show you how to use the Sklearn Logistic Regression function to create logistic regression models in Python. I’ll quickly review what logistic regression is, explain the...continue reading.
Sklearn make_classification, Explained
With the rise of AI, machine learning has suddenly become very popular. Machine learning has been around for decades, but machine learning systems are becoming increasingly important in a range...continue reading.
How to Make a Python Perceptron from Scratch
The Perceptron stands as one of the most basic building blocks for creating neural networks, including more advanced structures like deep networks and their variants. Originally developed in the late...continue reading.
Perceptrons, Explained
Unless you’ve been living in cave somewhere in remote Eurasia, you should know that deep learning is very popular, and very powerful. A variety of tools from self driving cars...continue reading.
Hugging Face Integrations
Hugging Face rapidly became a very popular platform to build, share and collaborate on deep learning applications. We have worked on integrating the torch for R ecosystem with Hugging Face...continue reading.
Tapping the Power of Supply Chain Data with Machine Learning
In the dynamic realm of supply chain management, one thing is clear: the ability to harness the power of data is the key to success. But with data pouring in...continue reading.
