Building a re-usable interactive and batch calculator for normal and binomial confidence intervals in Python and Jupyter Notebook — Background It is very common that we can obtain a sample of values, maybe a sample of 200 users clicking on our new web-site page or a sample of 30 voters in an exit poll on their way out of the voting station. In order to make a statement about these…

Outliers A boxplot showing the median and inter-quartile ranges is a good way to visualise a distribution, especially when the data contains outliers. The meaning of the various aspects of a box plot can be explained as follows - Generating some data We are going to need some test data to explore the issues…

Background There are many reasons why further education providers are increasing their focus on the alignment of supply of education vs. the demand for skills and qualifications of local employers and local economic need. The new Ofsted Education Inspection Framework (EIF) has a focus on the intent, implementation and impact of…

Exploring confidence intervals and hypothesis testing with a realistic worked example and easy-to-follow steps — Introduction In this excellent article, the author explores the p-value in statistics and uses the example of an archery team to compare two distributions. I found this article very insightful but I was left wanting more in that I wanted the statistics breaking down into simple steps that a non-statistician could…

How to fully understand stacking and use it effectively in machine learning by implementing a stacking model from scratch in Python and Jupyter — Background In my two recent articles on implementing ensemble machine learning algorithms using stacking I have explored how stacking works and how to build and understand a stacking algorithm in scikit-learn. A Deep Dive into Stacking Ensemble Machine Learning — Part I How to use stacking effectively in machine learning by fully understanding what stacking is and how it workstowardsdatascience.com

How to use stacking effectively in machine learning by implementing a stacking classifier in Python, Jupyter and Scikit-Learn — Background In my recent article on stacking I explored what stacking is and how it works by building up a visual workflow of the 4 main steps involved in creating a stacking model. A Deep Dive into Stacking Ensemble Machine Learning — Part I How to use stacking effectively in machine learning by fully understanding what stacking is and how it workstowardsdatascience.com

How to use stacking effectively in machine learning by fully understanding what stacking is and how it works — Background I had read about stacking in several books and also looked up stacking examples on Kaggle and on other web sites found by searching on line. It was clear from the reading and research that stacking had the potential to increase the accuracy of predictive algorithms and to improve leader…

How to fully understand how soft and hard voting works by building the algorithm that performs the voting from scratch — Background A little while ago I was reading a book called “Approaching (Almost) Any Machine Learning Problem” by Abhishek Thakur (see https://www.amazon.co.uk/Approaching-Almost-Machine-Learning-Problem-ebook/dp/B089P13QHT) when I came across some code that implemented soft and hard voting in ensemble learning.

How to build a high-performance emotional tone analyzer to compete with IBM Watson with no per-API transaction charges — Introduction In a recent article I showed how to use the IBM Watson “Tone Analyzer” which enables the emotional analysis of text to be directly embedded into machine learning applications written in Python (or other languages). How to Use the IBM Watson Tone Analyzer to Perform Emotional Sentiment Analysis in Python How to use the IBM Watson Artificial Intelligence platform to predict the primary emotion a customer felt when writing…towardsdatascience.com