17.2 writes it as a mixed number in lowest terms

.For small training sets variance may contribute more to the overall error than bias. Sometimes this is handled by reducing the

Ymorist [56]

Answer:

Bias is the difference between the average prediction of our model and the correct value which we are trying to predict and variance is the variability of model prediction for a given data p[oint or a value which tells us the spread of our data the variance perform very well on training data but has high error rates on test data on the other hand if our model has small training sets then it's going to have smaller variance & & high bias and its contribute more to the overall error than bias. If our model is too simple and has very few parameters then it may have high bias and low variable. As the model go this is conceptually trivial and is much simpler than what people commonly envision when they think of modelling but it helps us to clearly illustrate the difference bewteen bias & variance.

3 0

2 years ago

A business could not collect $5,000 that it was owed. The total owed to the business was $100,000. What fraction of the total wa

son4ous [18]

Total owed to the business = $100,000

amount that could not be collected = $5000

Fraction of total not collected