What is variance in Machine learning?
I am familiar with terms high bias and high variance and their effect on the model. Basically your model has high variance when it is too complex and sensitive to even outliers. But recently I was asked the meaning of the term Variance in machine learning model in one of the interviews?
I would like to know what exactly Variance means in ML Model and how it gets introduced in your model? I would really appreciate it if someone could explain this with an example.
Variance in machine learning is the variability of model prediction for a given data point or a value which tells us the spread of our data. Model with high variance pays a lot of attention to training data and does not generalize on the data which it hasn’t seen before. As a result, such models perform very well on training data but have high error rates on test data.
Error due to variance Error due to variance is the amount by which the prediction, over one training set, differs from the expected value over all the training sets. In machine learning, different training data sets will result in a different estimation. But ideally it should not vary too much between training sets. However, if a method has high variance then small changes in the training data can result in large changes in results. https://www.coursera.org/lecture/machine-learning/diagnosing-bias-vs-variance-yCAup https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229