Algorithms that can learn relationships between input data to make the most accurate predictions possible through different learning approaches.
What is bagging? Bagging is a parallel ensemble learning technique that trains multiple weak models on different datasets and averages their predictions. The bagging algorithm Problem statement We have a dataset and want to build an ensemble model to make…
What is ensemble learning? Ensemble learning is a machine-learning approach where we train numerous simple models and aggregate their predictions. These simple models are called weak learners. What is an ensemble model? As inferred from the definition above, an ensemble…
In this post, I’ll show you how to program a random forest from scratch in Python using ONLY MATH. Why is coding a random forest from scratch useful? When studying a new machine learning model, I always get lost between…
In this article, I’ll show you how to program linear regression from scratch in Python using ONLY MATH. Let’s get started. Why is coding linear regression from scratch useful? When I study a new machine learning model, I always get…
What linear regression is Linear regression is a supervised machine-learning algorithm that fits a straight line to the input data to represent the relationship between x and y. Linear regression structure Suppose we have a dataset that contains two columns,…
In this article, we’ll learn how to program a decision tree from scratch in Python using ONLY MATH. Let’s get started. Why is building a decision tree from scratch useful? When studying a new machine learning model, I always get…
In this article, I’ll show you how to program gradient descent from scratch in Python using ONLY MATH. Let’s get started. Why is coding gradient descent from scratch useful? When studying a new machine learning algorithm, I always get lost…
Disclaimer The gradient descent algorithm is based on the concept of the derivative of a function. Check this article before continuing. The gradient descent algorithm Gradient descent is a parameter optimization algorithm. Given an input function f(x), it finds the…
What is a random forest? A random forest is a bagging machine-learning model that combines the output of numerous decision trees to make predictions. Random forest training As said before, the random forest algorithm is just the bagging (bootstrap aggregation)…
What a decision tree is A decision tree is a non-parametric supervised machine learning model that predicts outputs by extracting various conditions from the data that an input may or may not meet. Decision tree structure The CART algorithm: how…