The Bias vs Variance Tradeoff [Math Mondays]
This is one of the most important concepts in Data Science and Machine Learning
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Getting back on topic, today’s topic will be about the bias-variance tradeoff. This is one of the most important statistical tradeoffs you could know about and it dictates a lot of decisions when it comes to Deep Learning. If you’re a manager leading data-driven teams, someone looking into Machine Learning, or just someone who wants to keep in touch with the most important developments/ideas in tech, you should know about this.
Key Highlights
What is Bias- Bias refers to the error of your Machine Learning Model. If your model predicted 3 for a value that is 2, then your bias is 1 (keep in mind ML has multiple ways of computing error, so the values might not be so straightforward).
What is Variance- I could give you the math formula, but that is unnecessary for the majority of you. Think of variance as the spread in your predictions. If your model has predicted [10,55,100,1] for similar inputs then your model has a high variance.
Why High Bias—> Underfitting- Underfitting happens when your model hasn’t learned the underlying pattern of your data fully. High bias —> that there is a lot about your data to learn. You can connect the dots.
Why Low Variance—> Overfitting- Overfitting happens when your model learns too well. How is this possible you ask? Keep in mind all datasets have an inherent noise, whether from measurement error, random mini fluctuations, design decisions, or any of the other moving parts. When overfitting, your model ends up learning this as well. This can lead to generalization problems because a dataset with different noise would throw it off.
Below is a video explaining this concept in more detail, with illustrations. It is made by yours truly, so you know the information in there is top-notch. Check it out if you’re interested.
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