Bais Variance — Machine learning

Bais and Variance play an important role in deciding which predictive model to use.

To get expected results from the model’s it is important in machine learning algorithms to understand the concept of Bias and Variance. As we know the main aim of any machine learning model is to evaluate the target function (f) when we have input data (x) for the output (y). So for better understanding of the working of machine learning algorithms, they are divided into three parts,

Bais

Variance

Irreducible Error

It is very difficult to reduce irreducible errors in any case. Here we shall be concentrating only on Bais and Variance.

Bais

Bais refers to the gap between the productive value by the model than the actual value. Bais cuts down the supposition which helps the model to understand the targeting function. Bais is easier to understand by dividing them into low-bais and high-bais. As we know linear algorithms have high bais which reduces their flexibility, it also makes it quick to learn and understand but that's not the same when it comes to complex problems.

Low Bais- Put forths less supposition of the formation of the target function.

High bais- Put forths more supposition of the formation of the target function.

Variance

Variance refers to the amount of how much they are scattered. In simpler form just by using different training data studying the approximately of the change in target function. As this algorithm works best in finding out the covered underlying mapping between input and output variables, it should not change a lot. Normally nonlinear machine learning algorithms which have good flexibility have high variance.

Low Variance- According to the changes in the data set it reduces the supposition change of the target function.

High Variance- According to the changes in the data set its increase in the supposition changes the target function.

Bais — Variance Trade-off

To get good prediction performance from a model it should have low bais and low variance in machine learning algorithms. In machine learning algorithms we cannot escape the relation between bias and variance. It all depends upon the choice of algorithms and finding a different balance. Increasing the bias will decrease the variance and increasing the variance will decrease the bias.

Hence it becomes important for AI startups to understand these concepts for delivering better products.