Here, we will go over what Bias error and Variance error are, sources of these errors and how you can work to reduce these errors in your model.
In the process of building a Predictive Machine learning model, we come across the Bias and Variance errors. The Bias-Variance Tradeoff is one of the most popular tradeoffs in Machine Learning. Here, we will go over what Bias error and Variance error are, sources of these errors and how you can work to reduce these errors in your model.
How does Machine Learning differ from traditional programming?
The high school definition of a program was simple. A program is a set of rules that tells the computer what to do and how to do it. This is one of the main difference between traditional programming and Machine Learning.
In traditional programming, the programmer defines the rules. The rules are usually well defined and a programmer often has to spend a good amount of time debugging code to ensure that the code runs smoothly.
In Machine Learning, while we still we still code, we do not define the rules. We build a model and feed it our expected results (supervised ML) or we allow the model come up with its own results (unsupervised ML). The main focus in Machine Learning is to improve the accuracy of the initial guess the model makes.
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If you run a learning algorithm and it doesn’t perform good as you are hoping, it will be because you have either a high bias problem or a high variance problem.
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
A practical implementation to demonstrate bias and variance trade off using mlxtend library