Saul Dobilas

Machine Learning

An intuitive explanation of the Adaptive Boosting algorithm and its difference from other Decision Tree based Machine Learning algorithms

Image for post
Image for post
Image by author.

Intro

The number of Machine Learning algorithms keeps increasing with time. If you want to be a successful Data Scientist, you must understand how they differ from one another.

This story is part of a series where I provide an in-depth look at different algorithms, how they work, and how to build them in Python.

The story covers the following topics:

  • Visual comparison of model predictions between a Single Decision Tree, Random Forest, and AdaBoost.
  • Explanation of how AdaBoost differs from other algorithms
  • Python code examples

Decision Tree vs. Random Forest vs. AdaBoost

Let us start with a comparison of the prediction probability surface made by each of the three models. …


Machine Learning

A detailed explanation of how random forest machine learning algorithm works, what makes it superior to decision trees, and how you can build them in Python

Image for post
Image for post
Random Forest. Image by author.

Intro

If you want to be a successful Data Scientist, you need to understand the nuances of different Machine Learning algorithms.

This story is part of a series where I provide an in-depth look at how such algorithms work. This includes simple examples, 3D visualizations, and complete Python code for you to use in your Data Science projects.

The story covers the following topics:

  • The category of algorithms Random Forest classification belongs to
  • An explanation of how Random Forest classification works and why it is better than a single decision tree
  • Python code examples and visualizations

What category of algorithms does it belong to?

Similar to some other algorithms, Random Forest can handle both classification…


Getting Started, Machine Learning

How does the CART algorithm work, and how to successfully use it in Python?

Image for post
Image for post
CART model prediction surface. See how the chart was made in the Python section at the end of this story. Image by author.

Intro

If you want to be a successful Data Scientist, it is essential to understand how different Machine Learning algorithms work.

This story is part of the series that explains the nuances of each algorithm and provides a range of Python examples to help you build your own ML models. Not to mention some cool 3D visualizations!

The story covers the following topics:

  • The category of algorithms that CART belongs to
  • An explanation of how the CART algorithm works
  • Python examples on how to build a CART Decision Tree model

What category of algorithms does CART belong to?

As the name suggests, CART (Classification and Regression Trees) can be used for both classification and regression…


Machine Learning

A complete explanation of the inner workings of Support Vector Machines (SVM) and Radial Basis Function (RBF) kernel

Image for post
Image for post
SVM with RBF kernel and high gamma. See how it was created in the Python section at the end of this story. Image by author.

Intro

It is essential to understand how different Machine Learning algorithms work to succeed in your Data Science projects.

I have written this story as part of the series that dives into each ML algorithm explaining its mechanics, supplemented by Python code examples and intuitive visualizations.

The story covers the following topics:

  • The category of algorithms that SVM classification belongs to
  • An explanation of how the algorithm works
  • What are kernels, and how are they used in SVM?
  • A closer look into RBF kernel with Python examples and graphs

What category of algorithms does Support Vector Machines classification belong to?

Support Vector Machines (SVMs) are most frequently used for solving classification problems, which fall under the supervised machine…


Machine Learning

A detailed explanation of the theory behind the algorithm together with 6 Python examples

Image for post
Image for post
Naive Bayes Model Decision Boundaries. Image by author. (See section 5 for how this graph was made).

Preface

Just so you know what you are getting into, this is a long story that contains a mathematical explanation of the Naive Bayes classifier with 6 different Python examples. Please take a look at the list of topics below and feel free to jump to the most interesting sections for you.

Intro

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. …


Machine Learning

A detailed explanation of the algorithm together with useful examples on how to build a model in Python

Image for post
Image for post
Logistic Regression. Image by author. (See how this graph was made in the Python section below)

Preface

Just so you know what you are getting into, this is a long story that contains a visual and a mathematical explanation of logistic regression with 4 different Python examples. Please take a look at the list of topics below and feel free to jump to the sections that you are most interested in.

Intro

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. …


Machine Learning

A visual explanation of SVR with Python implementation examples

Image for post
Image for post
Support Vector Regression. Image by author.

Intro

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it also provides you with Python examples to build your own ML models.

This story covers the following topics:

  • The category of algorithms that SVR belongs to
  • An intuitive explanation of how SVR works
  • A few examples of how to build SVR models in Python

What category of algorithms does Support Vector Regression belong to?

While you may not be familiar with SVR, chances are you have previously…


Machine Learning

A detailed guide to using Locally Weighted Scatterplot Smoothing (LOWESS) algorithm in Python

Image for post
Image for post
LOWESS algorithm finding the trend. Image by author.

Intro

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it also provides you with Python examples to build your own ML models.

This story covers the following topics:

  • What category of algorithms does LOWESS belong to?
  • How does Locally Weighted Scatterplot Smoothing work?
  • How can I use LOWESS to identify patterns and predict new data in Python?

What category of algorithms does LOWESS belong to?

Locally Weighted Scatterplot Smoothing sits within the family of regression…


Hands-on Tutorials, Machine Learning

A visual explanation of the MARS algorithm with Python examples and comparison to linear regression

Image for post
Image for post
Model prediction comparison between MARS and Linear Regression. Image by author.

Intro

Machine Learning is making huge leaps forward, with an increasing number of algorithms enabling us to solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it also provides you with Python examples to build your own ML models.

Before we dive into the specifics of MARS, I assume that you are already familiar with Linear Regression. If you would like a refresher on the topic, feel free to explore my linear regression story:

This story covers the following topics:

  • What category of algorithms…


All you need to know about building Machine Learning models using the linear regression algorithm

Image for post
Image for post
Multiple linear regression. Image by author.

Intro

Machine Learning is making huge leaps forward, with an increasing number of algorithms available so we can solve complex real-world problems.

This story is part of a deep dive series explaining the mechanics of Machine Learning algorithms. In addition to giving you an understanding of how ML algorithms work, it will also provide you Python examples so you can use them to build your own ML models.

What is covered in this story?

  • What category of algorithms does linear regression belong to?
  • What problems can be solved using linear regression?
  • How does the linear regression algorithm work?
  • How can I use linear regression to build a…

Saul Dobilas

Data Science and Analytics Professional

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store