What is Bias-Variance Tradeoff?

In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters. The variance is an error from sensitivity to small fluctuations in the training set.

Why is Bias-Variance Tradeoff important?

The Bias-Variance Tradeoff is relevant for supervised machine learning – specifically for predictive modeling. It’s a way to diagnose the performance of an algorithm by breaking down its prediction error.

What is the Bias-Variance Tradeoff in the training of machine learning models?

If a model follows a complex machine learning model, then it will have high variance and low bias( overfitting the data). This tradeoff in complexity is what is referred to as bias and variance tradeoff. An optimal balance of bias and variance should never overfit or underfit the model.

How do you balance bias and variance?

Balancing Bias And Variance

1. Choose appropriate algorithm.
2. Reduce dimensions.
3. Reduce error.
4. Use regularization techniques.
5. Use ensemble models, bagging, resampling, etc.
6. Fit model parameters, e.g., find the best k for KNN, find the optimal C value for SVM, prune decision trees.
7. Tune impactful hyperparameters.

What is the bias in machine learning?

Machine learning bias, also sometimes called algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systemically prejudiced due to erroneous assumptions in the machine learning process.

What is bias and variance with example?

An example of the bias-variance tradeoff in practice. On the top left is the ground truth function f — the function we are trying to approximate. To fit a model we are only given two data points at a time (D’s). The error between f and ğ represents the bias.

What is the bias-variance trade off to address bias and variance?

An algorithm can’t be more complex and less complex at the same time. To build a good model, we need to find a good balance between bias and variance such that it minimizes the total error. An optimal balance of bias and variance would never overfit or underfit the model.

What is bias-variance tradeoff?

If the bias increases, the error calculated increases. (As seen on the bottom-left circle in the image). High variance and high bias indicate that data is spread with a high error. (As seen on the bottom-right circle in the image) This is Bias-Variance Tradeoff.

How do I configure the bias-variance trade-off for specific algorithms?

Below are two examples of configuring the bias-variance trade-off for specific algorithms: The k-nearest neighbors algorithm has low bias and high variance, but the trade-off can be changed by increasing the value of k which increases the number of neighbors that contribute t the prediction and in turn increases the bias of the model.

What does it mean to lower the bias and variance?

Lowering both Bias and Variance means reducing the total error in the model. It also means creating a model that is not too simple and not too complex. Model complexity is something that we also look at when considering the Bias and Variance.

What is the difference between bias and variance in machine learning?

Bias is an error between the actual values and the model’s predicted values. Variance is also an error but from the model’s sensitivity to the training data. If we were to aim to reduce only one of the two then the other will increase. A prioritization of Bias over Variance will lead to a model that overfits the data.