Category: Data Science

Harris Amjad explains a common algorithm for classification:

It so happens that given the hype of Machine Learning (ML) and especially Large Language Models these days, there is a considerable proportion of those who wish to understand how these systems work from scratch. Unfortunately, more often than not, the interest fades away quickly as learners jump to complicated algorithms like neural networks and transformers first, without giving heed to traditional ML algorithms that paved the foundation for these advanced algorithms in the first place. In this tip, we will introduce and implement the K-Nearest Neighbors model in Python. Although it is quite old, it remains very popular due to its simplicity and intuitiveness.

Click through to learn more about this algorithm, including an implementation from scratch in Python.

Vinod Chugani continues a series on tree-based classification techniques:

LightGBM is a highly efficient gradient boosting framework. It has gained traction for its speed and performance, particularly with large and complex datasets. Developed by Microsoft, this powerful algorithm is known for its unique ability to handle large volumes of data with significant ease compared to traditional methods.

In this post, we will experiment with LightGBM framework on the Ames Housing dataset. In particular, we will shed some light on its versatile boosting strategies—Gradient Boosting Decision Tree (GBDT) and Gradient-based One-Side Sampling (GOSS). These strategies offer distinct advantages. Through this post, we will compare their performance and characteristics.

Read on to learn more about LightGBM as an algorithm, as well as how to use it.

Vinod Chugani is missing a few data points:

XGBoost has gained widespread recognition for its impressive performance in numerous Kaggle competitions, making it a favored choice for tackling complex machine learning challenges. Known for its efficiency in handling large datasets, this powerful algorithm stands out for its practicality and effectiveness.

In this post, we will apply XGBoost to the Ames Housing dataset to demonstrate its unique capabilities. Building on our prior discussion of the Gradient Boosting Regressor (GBR), we will explore key features that differentiate XGBoost from GBR, including its advanced approach to managing missing values and categorical data.

Read on to see how it fares.

Vinod Chugani compares two techniques for working with trees:

Ensemble learning techniques primarily fall into two categories: bagging and boosting. Bagging improves stability and accuracy by aggregating independent predictions, whereas boosting sequentially corrects the errors of prior models, improving their performance with each iteration. This post begins our deep dive into boosting, starting with the Gradient Boosting Regressor. Through its application on the Ames Housing Dataset, we will demonstrate how boosting uniquely enhances models, setting the stage for exploring various boosting techniques in upcoming posts.

Read on for more information. The neat part about the “boosting versus bagging” debate is that both techniques are quite useful. Although boosting (via algorithms like XGBoost or LightGBM) is the more popular technique, bagging (random forest) is extremely powerful in its own right.

Harris Amjad does some text cleanup:

Natural Language Processing (NLP) is currently all the rage in the current machine learning landscape. With technologies like ChatGPT, Gemini, Llama, and so many other state-of-the-art text generators getting popular with the mainstream public, many newcomers are pouring into the field of NLP. Unfortunately, before we delve into how these fancy chatbots work, we must understand how we are engineering and treating our data before we feed it to our model. In this tip, we will introduce and implement some basic text preprocessing and cleaning techniques with Python.

Click through for some common operations. Some of these are very important for certain tasks but likely unhelpful for others. That could include things like lower-casing all words or removing stopwords. There are also some operations like spell checking and jargon expansion (or replacement) that you will likely want to include in a real-life project with actual people entering the data, versus a tidy sample dataset.