Bias‑Variance Tradeoff Explained
Key Points
- The speaker illustrates underfitting and overfitting with simple graphs, showing that too few training epochs leave the model unable to capture the data, while too many epochs cause it to memorize every point.
- Bias is described as the systematic error between predictions and true values; high bias oversimplifies the data and leads to underfitting.
- Variance is the variability of predictions across the dataset; high variance causes the model to memorize training points and results in overfitting.
- A model with high bias and low variance underfits, whereas a model with high variance and low bias overfits, so the goal is to achieve both low bias and low variance.
- The bias‑variance trade‑off is visualized as total error versus model complexity, where increasing complexity reduces bias but raises variance, and the optimal model balances the two to minimize overall error.
Sections
Full Transcript
# Bias‑Variance Tradeoff Explained **Source:** [https://www.youtube.com/watch?v=tUs0fFo7ki8](https://www.youtube.com/watch?v=tUs0fFo7ki8) **Duration:** 00:04:34 ## Summary - The speaker illustrates underfitting and overfitting with simple graphs, showing that too few training epochs leave the model unable to capture the data, while too many epochs cause it to memorize every point. - Bias is described as the systematic error between predictions and true values; high bias oversimplifies the data and leads to underfitting. - Variance is the variability of predictions across the dataset; high variance causes the model to memorize training points and results in overfitting. - A model with high bias and low variance underfits, whereas a model with high variance and low bias overfits, so the goal is to achieve both low bias and low variance. - The bias‑variance trade‑off is visualized as total error versus model complexity, where increasing complexity reduces bias but raises variance, and the optimal model balances the two to minimize overall error. ## Sections - [00:00:00](https://www.youtube.com/watch?v=tUs0fFo7ki8&t=0s) **Untitled Section** - - [00:03:07](https://www.youtube.com/watch?v=tUs0fFo7ki8&t=187s) **Finding the Ideal Model Complexity** - The speaker explains that increasing model complexity lowers bias but raises variance, and the optimal solution is to select a complexity that balances both to minimize total error and avoid over- or underfitting. ## Full Transcript
As a machine learning engineer,
you may have experienced this dilemma.
You've cleaned and processed your data
and now it's time to train your machine learning model.
Let's draw an example of what your graph might look like.
For a data set with these following points as an example,
you're probably expecting a graph that looks like this.
However, after training your machine learning model,
you find out that it looks like this.
So obviously the data is underfitting
and the model wasn't able to learn the training data well enough.
So we can fix that by training the data for a longer amount of time.
However, now it looks like this.
It's fitting almost every single data point exactly.
So it looks like the model has learned the training data a little too well.
Well, why does this happen?
In a previous video in our channel,
we talked about how overfitting and underfitting
can affect machine learning models.
But let's dive deeper into the root cause of the problem,
which is bias and variance.
So what do those terms mean?
Bias and variance are two types of error
that can lead to underfitting or overfitting
in machine learning models.
Let's talk about bias first.
Bias can be defined as the difference between the predicted values
and the actual values, also known as the ground truth.
When the bias is high,
the model fails to recognize patterns in the data
and it starts to oversimplify the data.
When the data is oversimplified so much
that it's not able to recognize patterns or complexities at all,
we can call that underfitting.
Let's talk about variants next.
Variance can be defined
as the variability in predictions for each value in the data set.
When the variance is high,
the model basically memorizes all of the points
in the training data set,
instead of memorizing the overall complexity
and patterns behind the data.
When this happens, we call that overfitting.
In short, a model with high bias
and low variance will tend to underfit.
And on the other hand,
a model with high variance and low bias will tend to overfit.
We don't want our graph to look like either one of these graphs.
Ideally, we want a model that has both low bias and low variance.
In other words, we want a model that is able to recognize
complexities and patterns in the training data,
but also on data that it hasn't seen before.
This is known as the bias variance trade off.
All right, let's take a closer look at a graph that you may have seen before
that illustrates the bias variance trade off.
This is a graph that shows
how the total amount of error changes
as model complexity increases.
We can think of model complexity
as a way to measure how well a model is able to recognize
relationships and patterns in data.
We notice that as the model complexity increases,
the total amount bias decreases.
We also notice that as the model complexity increases,
that the amount of variance increases.
And as the variance and bias change,
the total amount of error also changes.
So our overall goal
is to minimize both the variance and bias
such that we can get the lowest amount of error,
and that will usually be in this sweet spot right here.
This is our ideal complexity.
So, in short, the best way to fix and prevent
overfitting and underfitting is to find the ideal complexity in the model
that allows you to reduce both variance and bias,
while also reducing the total amount of error.
Thanks for watching
and as always, please remember to like and subscribe.