How Neural Networks Learn, with 1 Complete Example
Gain insight, and an intuition, for how Neural Networks learn. A worked example, covering each step in the learning procedure, is included.
Supervised Models
This category groups articles dealing with supervised problems. Each post focuses on either a specific supervised algorithm, or a tool used when tackling a supervised problem. The emphasis here is on understanding these models and techniques at a technical level. Here you will learn to build supervised models in Python from scratch.
A Simple Introduction to Cross Entropy Loss
Cross Entropy serves as a loss function, in the context of machine learning classification problems. Learn all about the Cross Entropy Loss here.
Understanding Backpropagation
This post will cover the Backpropagation algorithm used to train supervised Neural Networks. Motivations and mathematical details will be covered.
Implement the Perceptron Algorithm from Scratch in Python
This article covers an implementation of the Perceptron Algorithm from Scratch. The model is first described, and then built & tested in Python.
Using Decision Trees for Clustering In 1 Simple Example
Can Decision Trees be used for clustering? This post will outline one possible application of Decision Trees for clustering problems.
8 Key Advantages and Disadvantages of Decision Trees
We will outline 8 key advantages and disadvantages of Decision Trees in this post. Both classification and regression Decision Trees will considered.
Pruning Decision Trees in 3 Easy Examples
Pruning Decision Trees involves a set of techniques that can be used to simplify a Decision Tree, and enable it to generalise better.
Can Decision Trees Handle Categorical Features?
Can Decision Trees Handle Categorical Features? Yes, Decision Trees handle categorical features naturally. Often these features are treated by first one-hot-encoding (OHE) in a preprocessing step. However, it is straightforward to extend the CART algorithm to make use of categorical features without such preprocessing. In this post, I will implement classification and regression Decision Trees capable …
Can Decision Trees Handle Missing Values?
Can Decision Trees Handle Missing Values? Yes, Decision Trees handle missing values naturally. It is straightforward to extend the CART algorithm to support the handling of missing values. However, attention needs to be made regarding how the algorithm is implemented in code. In this post, I will implement classification and regression Decision Trees capable of dealing …
Median Absolute Error
Median Absolute 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 …