Interpreting

An accessible walkthrough of fundamental properties of this popular, yet often misunderstood metric from a predictive modeling perspectivePhoto by Josh Rakower on UnsplashR² (R-squared), also known as the coefficient of determination, is widely used as a metric to evaluate the performance of regression models. It is commonly used to quantify goodness of fit in statistical modeling, and it is a default scoring metric for regression models both in popular statistical modeling and machine learning frameworks, from…

Learn about data-centric explanation and its different types in this articleSource: PixabayExplainable AI (XAI) is an emerging concept that aims to bridge the gap between AI and end-users, thereby increasing AI adoption. XAI can make AI/ML models more transparent, trustworthy, and understandable. It is a necessity, especially for critical domains such as healthcare, finance, and law enforcement.To get an introduction to XAI, the following 45 minutes presentation of mine from the AI Accelerator Festival APAC, 2021 will be…

How to Use Integrated Gradients to Interpret BERT Model’s PredictionPhoto by Shane Aldendorff: https://www.pexels.com/photo/shallow-focus-photography-of-magnifying-glass-with-black-frame-924676/Bidirectional Encoder Representation from Transformer or BERT is a language model that’s very popular within the NLP domain. BERT is literally the swiss army knife of NLP due to its versatility and how well it performed in many different NLP tasks, such as text classification, named entity recognition, question-answering, etc.But…

An assessment of the real-world constraints of cloud migrationsPhoto by Kateryna Babaieva from Pexels.comLet’s imagine Company A. Company A is a typical mid-tier industry leader. They already have a data science team, a very successful ML deployment, and a strong data infrastructure.Additionally, compared to their competitors, they already have some experts in both Cloud and data engineering. They even have their leadership team committing to a “Cloud-first” strategy but have nicely provided the teams with flexibility on…

How to determine the order of AR and MA modelsImage by the author via KaggleAutocorrelation analysis is an important step in the Exploratory Data Analysis of time series forecasting. The autocorrelation analysis helps detect patterns and check for randomness. It’s especially important when you intend to use an autoregressive–moving-average (ARMA) model for forecasting because it helps to determine its parameters. The analysis involves looking at the Autocorrelation Function (ACF) and Partial Autocorrelation Function…

An intuitive explanationPhoto by Camylla Battani on UnsplashIn my previous article, I discussed why you should prefer reporting 95% confidence interval over p-value, especially when you are explaining the findings of your study to non-statistician readers/audiences. In this article, I continue the discussion further and try to provide a bit more clarity.Regarding the “correct” interpretation of the confidence interval, one of my readers asked a great question:As a student, I had the same question for a long time. Indeed,…

Regression coefficients alone do not tell you what you need to knowLogistic regressions are always praised for their interpretability, but in practice they are often misunderstood. In these cases it is usually assumed that coefficients in the logistic model work the same way as those in a multiple linear regression model. But this is not the case. Below, we will have a detailed look at the correct interpretation of logistic regressions and the mechanics that influence the effect of the features.Logistic regression was…

An intuitive explanation of the regression estimatorPhoto by Samia Liamani on UnsplashIn case you have taken an introductory applied econometrics/statistics/data science course, you must be familiar with the interpretation of the coefficient of a predictor in a linear model estimated using regression. Today, I am going to share an intuitive example to highlight the fact that — just like this author — you (probably) passed the introductory course/courses with a misunderstanding of the coefficient of interest.**This article…

Ever wondered how data tell stories? Visualisations narrate them.In 2017, data surpassed oil to become the most valuable asset on this planet. Data is generated in every sector in abundant amounts. According to cloud tweaks, at least 2.5 quintillion bytes of data is produced every day (that’s 2.5 followed by a staggering 18 zeros). That’s amazing. But, the human brain is incapable to indulged even 1% of such huge data. So, to bring the flattered files of data to life and turn them into information, we need exploratory…