Bias-Variance TradeOff | Bias Variance in Machine Learning | Machine Learning Tutorial | Intellipaat
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In this video, you will learn about a fundamental principle of machine learning, the variance tradeoff, which illustrates the balance between bias and variance in a model. Bias Variance in Machine Learning is important as it directly influences a model's ability to generalize to new, unseen data. The video on Bias-Variance Tradeoff starts with the basics, explaining bias and variance, and then delves into the concept of the bias-variance tradeoff. Finally, we explore methods to reduce the bias-variance tradeoff.
🔵 Following topics are covered in this session:
00:00 - Introduction
01:48 - What is Bias?
02:52 - What is Variance?
04:23 - What is the Bias variance Tradeoff?
11:48 - How to reduce the Tradeoff?
13:07 - Conclusion
✅What is Bias and Variance in simple words?
Bias is an error from overly simplistic models; the variance is from overly complex models. The bias-variance tradeoff seeks a balanced model for accurate predictions of new data.
✅ What is bias in linear regression?
In linear regression, bias refers to the difference between the predicted values of the model and the true values in the underlying data. A high bias indicates that the model oversimplifies the relationship, leading to systematic errors and potentially inaccurate predictions.
✅ What is the tradeoff equation for bias and variance?
The bias-variance tradeoff is a fundamental concept in machine learning. It represents the compromise between model simplicity (bias) and flexibility (variance). Increasing model complexity reduces bias but increases variance, and vice versa. The goal is to find the optimal balance for better predictive performance on unseen data.
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