If an individual is attending a job interview to work in data science, he may very well be asked to build a decision tree classifier on the iris dataset where the dependent variable is the species and the independent variable is the rest of the columns in the dataset. A…

In my last blog post I discussed how to create a decision tree classifier in Jax, a Python library created by Google. In this post I intend to fit the algorithm into a machine learning project, to show how it works. I have used sklearn’s wine dataset as the data…

During the last few weeks I have been working on learning Jax, which is a Python library developed by Google Brain in 2018. There is not a great deal of sample code available for Jax, as Tensorflow and Sklearn appear to be more popular at this time. …

I have been spending the last few weeks studying Python’s Jax library, which is set to overtake Tensorflow, for research purposes anyway. Google JAX is a machine learning framework for transforming numerical functions. It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function…

One of the great things about Kaggle is the fact they have the playground series of competitions that can help prospective data scientists to improve their programming skills. The most recent competition, 3.22, focused on the health of horses, specifically whether they lived, died, or were euthanized. In this…

Bayes theorem, named after the 18th century mathematician Thomas Bayes, is a mathematical formula determining conditional probability. Conditional probability is the likelihood of an outcome occurring based on a previous outcome having occurred in similar circumstances. Bayes theorem requires the user to update the predicted probabilities of an event by…

In my last post I discussed the basics of the Naive Bayes Classifier using the Jax library, and in this post I intend to put the algorithm into a machine learning project to demonstrate how it works. I have written the program used in this machine learning project in Python…

The Naïve Bayes classifier is a supervised machine learning algorithm, which is used for classification tasks. It is part of a family of generative learning algorithms, meaning that it seeks to model the distribution of inputs of a given class or category. Naïve Bayes is also known as a probabilistic…

In my recent post I discussed the theory of the softmax algorithm and how to use it to make predictions on a multinomial logistic regression problem. In this post I intend to put all the pieces together and create a machine learning project for which the softmax algorithm is used. …