Is a high variance in data good or bad?

Higher variance directly means that the data spread is big and the feature has a variety of data. Usually, high variance in a feature is seen as not so good quality.

In machine learning, a high variance in data typically refers to a situation where the model is overly sensitive to small fluctuations in the training data. This can lead to the model fitting too closely to the training data and performing poorly on unseen data, a phenomenon known as overfitting.

In general, high variance is considered bad because it indicates that the model has learned to capture noise or random fluctuations in the training data rather than the underlying patterns or relationships. As a result, the model’s performance may suffer when applied to new, unseen data.

To address high variance, techniques such as regularization, cross-validation, and increasing the amount of training data can be employed to help the model generalize better to unseen data and reduce overfitting.