Waht is Bias-Variance Tradeoff? | Intellipaat

The goal of every supervised machine learning is to minimise bias and variation while producing predictions with high accuracy.

The k-nearest neighbour method has a low bias but a high variance; however, the trade-off may be changed by increasing the value of k, that boosts the number of friends who participate to the forecast and, in turn, raises the biases of the models.

The support vector machine (svm) classifier technique has a low bias and a high variance; however, the trade-off can be changed by raising the C variable, which influences the number of margin breaches permitted in the training data, increasing the bias but reducing the variance.

It is impossible to ignore the relationship between bias and variation in machine learning. If the bias is raised, the variance will go down. Bias will become less pronounced as variety rises.