What is Bias Variance Tradeoff in Machine Learning?
In machine learning, the bias-variance tradeoff refers to the problem of finding the right balance between underfitting and overfitting a model. Bias refers to the error introduced by approximating a real-life problem with a simplified model, while variance refers to the error introduced by sensitivity to small fluctuations in the training data.
The video will explain how to achieve the optimal balance between bias and variance in a machine learning model. It will discuss how increasing model complexity can lead to lower bias but higher variance, and how reducing model complexity can lead to higher bias but lower variance. The goal is to find the right level of complexity that minimizes the overall error of the model.
Overall, this video will provide a clear understanding of the bias-variance tradeoff and how to optimize the performance of machine learning models.
I hope you will like this video.
Soumyabrata Roy LinkedIn: / soumyabratar