Soundness of mind

What does high variance mean in machine learning?

In machine learning, high variance refers to a model’s tendency to overfit the training data. This means that the model is too closely tuned to the training data, and is not generalizing well to new data points. High variance models are more likely to make inaccurate predictions on unseen data. To reduce variance, you need to increase the complexity of the model or reduce the amount of input features. Alternatively, you can use regularization techniques such as L1 and L2 regularization to reduce the complexity of the model.

Is high variance good machine learning?

Generally, high variance in a machine learning model is undesirable as it indicates that the model is overfitting to the training data. This means that the model is capturing too much of the noise from the dataset and not generalizing well to unseen data. To improve the performance of a machine learning model, it is important to use techniques like regularization and cross-validation to reduce the variance and improve the generalizability of the model.

What does it mean by having high variance?

High variance means that the data points in a dataset are far away from the average value. It indicates that the data points are spread out over a wide range of values, rather than being clustered around a central value. High variance is often associated with increased risk, as the data points in the dataset can be quite unpredictable. Generally, it is desirable to have low variance, as this suggests that the data points are more predictable and consistent.