Tag: bias Polynomial regression- Absence of a perfect model to get a job

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… 🏆 Bias – Variance Tradeoff

‘As a Data Scientist – should I be a specialist or generalist? After all, data science is an ocean!’ As someone who was in his first semester pursuing his Master’s in Analytics degree, this is the question I had in my mind after the professors’ introduced a plethora of new terminologies to me in every…

Bias and Variance – the struggle of daily life

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…

Bias-Variance Dilemma

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…

Discuss the trade off between bias and variance

Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm you’re 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… Bias/Variance trade-off is an important concept in learning theory. This post discusses topic from both – theoretical and practical perspective. General Main goal of any learning algorithm is to predict and generalise well. More formally, this goal is equivalent to ‘minimise expected error on unseen data’ – thus taking closer look at the components of…

Trade off between bias and variance

Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. By: Sachin Narang

Bias vs Variance – Much like raising a child

The two errors that are critical to understand model prediction are: Bias and Variance. These concepts can be applied to nearly all sorts of learning in our life. For example, the higher-level understanding of these two concepts can be done through the analogy of raising(training) a young child. Let, Home = Training set Instill best… Bias Variance and Tradeoff

Meaning of Bias and Variance? Bias: how unfair is something towards other Variance: how likely something changes with respect to others Thing to remember? High Bias: under fitting High Variance: over fitting What is High Bias and High Variance? Assume there are two examiners determining weather forecast. Where it rains only when it is humid… The Bias and Variance tradeoff

Developing the machine learning models isn’t so hard nowadays, due a lot of frameworks and libraries that give build-in functions to easily implement some fancy model. However, it’s important to understand the theory behind all practical implementation in order to build more accurate models. So in this topic will be discussed the theoretical understanding of…