Variance :

Variance in machine learning is how much a model's predictions change when trained on different subsets of the training data. It's a measure of how much the model relies on the training data to make predictions.

A model with high variance learns the training data too well, including the noise in the data. This means that the model won't be able to generalize well to new data that it hasn't seen before.

A model with low variance doesn't learn the training data as well, but it's less likely to overfit. This means that the model will be able to generalize better to new data.

To reduce variance, you can use a simpler model or more training data.