This is my first submission to #datacatedweekly and possibly my first article. I am currently on a job hunt in analytics and for this article I will present a scenario of using polynomial regression for the same. Gone are the times where you are considered for an interview based on single factor. Today, there are…
Read more
âAs a Data Scientist â should I be a specialist or generalist? After all, data science is an ocean!â As someone who was in his first semester pursuing his Masterâs in Analytics degree, this is the question I had in my mind after the professorsâ introduced a plethora of new terminologies to me in every…
Read more
Bias refers to the error that is introduced by approximating a real-life problem, which may be extremely complicated, by a much simpler model. Variance refers to the amount by which the prediction of model would change if we estimated it using a different training data set. The challenge lies in finding a method for which…
Read more
When I actually started my journey in Data Science, it was always difficult for me to remember the difference between Bias and Variance. We always talk about the Bias-Variance tradeoff when we talk about the model prediction. My post will present a very basic understanding of these terms and two related terms – Underfitting and…
Read more
Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm youâre using. This can lead to the model underfitting your data, making it hard for it to have high predictive accuracy and for you to generalize your knowledge from the training set to the test set. Variance is error due to too much…
Read more