This category groups posts on metrics used in machine learning. Each post focuses on a specific metric. The emphasis is on understanding how these tools actually work at a technical level. Here you will learn how to use machine learning metrics so that you can properly assess, and interpret, your models performance.
This post touches on an area of growing interest in AI: Global Model Explainability. Two different approaches will be investigated in a Jupyter notebook: summed SHAP values & SAGE.
Testing 2 Techniques for Global Model Explainability Read More »
We will cover the ROC and PR area under the curve metrics for evaluating a simple classifier.
Area Under the Curve Explained with 2 Simple Examples Read More »
Shapley Values can help explain how any machine learning model works. This post covers how they can be estimated in Python from scratch.
Shapley Values for Machine Learning Read More »
Cross Entropy serves as a loss function, in the context of machine learning classification problems. Learn all about the Cross Entropy Loss here.
A Simple Introduction to Cross Entropy Loss Read More »
Mean Squared Error What is the Median Absolute Error? The Median Absolute Error is a metric that can be used to quantify a regression models performance. This measure is slightly more difficult to interpret for a non-technical audience. However, the main benefit of using this quantity is its strong resilience to outliers. This is in
Median Absolute Error Read More »
Can Decision Trees Handle Missing Values? For classification problems, information gain in Decision Trees is measured using the Shannon Entropy. The amount of entropy can be calculated for any given node in the tree, along with its two child nodes. The difference between the amount of entropy in the parent node, and the weighted average
How to Measure Information Gain in Decision Trees Read More »
Understanding Backpropagation For those who prefer a video presentation, you can see me work through the material in this post here: https://youtu.be/WEJcETfWwOo What are Precision@k and Recall@K ? Precision@k and Recall@k are metrics used to evaluate a recommender model. These quantities attempt to measure how effective a recommender is at providing relevant suggestions to users. The
Precision@k and Recall@k Made Easy with 1 Python Example Read More »
Can Decision Trees Handle Missing Values? This article will cover the Gini Impurity: what it is and how it is used. To make this discussion more concrete, we will then work through the implementation, and use, of the Gini Impurity in Python. What is the Gini Impurity? The Gini Impurity is a loss function that
Explaining the Gini Impurity with Examples in Python Read More »
How Neural Networks Learn, with 1 Complete Example This post will discuss various Cross Validation techniques. Cross Validation is a testing methodology used to quantify how well a predictive machine learning model performs. Simple illustrative examples will be used, along with coding examples in Python. What is Cross Validation? A natural question to ask, when building
A Complete Introduction to Cross Validation in Machine Learning Read More »
Neural Networks Explained Simply In this post, we will cover how to measure performance of a classification model. The methods discussed will involve both quantifiable metrics, and plotting techniques. How do we Measure Performance of a Classification Model? Classification is one of the most common tasks in machine learning. This is the case where the
6 Methods to Measure Performance of a Classification Model Read More »