![]() Weak learners are sure about particular part of a problem. They have high variance and they don’t usually overfit ![]() They have high bias, so they can not solve hard learning problemsģ. They have low variance and they don’t usually overfitĢ. Which of the following is / are true about weak learners used in ensemble model?ġ. So, creating an ensemble of diverse models is a very important factor to achieve better results. Note: All individual models have meaningful and good predictions.Īn ensemble is an art of combining a diverse set of learners (individual models) together to improvise on the stability and predictive power of the model. ![]() True or False: Ensembles will yield bad results when there is significant diversity among the models. But, you can use an ensemble for unsupervised learning algorithms also. Generally, we use ensemble technique for supervised learning algorithms.
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