Final answer:
To tackle a model suffering from low bias and high variance, algorithms such as regularization and ensemble learning can be used.
Step-by-step explanation:
When a model suffers from low bias and high variance, it means that the model is overfitting to the training data, resulting in poor generalization to new data. To tackle this problem, one algorithm that can be used is regularization, specifically L1 or L2 regularization. Regularization adds a penalty term to the error function, which helps to control the complexity of the model and reduce overfitting.
Another algorithm that can be used is ensemble learning, such as random forests or gradient boosting. Ensemble learning combines multiple models to make predictions, which can help improve the accuracy and reduce the variance of the overall model.
It's important to note that the choice of algorithm depends on the specific problem and the data at hand. It may require experimenting with different algorithms and adjusting their parameters to find the optimal solution.