A strong classifier is a model for binary classification that performs with arbitrary performance, much better than random guessing. This is sometimes interpreted to mean perfect skill on a training or holdout dataset, although more likely refers to a “ good ” or “ usefully skillful ” model.
- Strong Classifier : Formally, a classifier that achieves arbitrarily good accuracy.
We seek strong classifiers for predictive modeling problems. It is the goal of the modeling project to develop a strong classifier that makes mostly correct predictions with high confidence.
Again, although the concept of a strong classifier is well understood for binary classification, it can be generalized to other problem types and we can interpret the concept less formally as a well-performing model, perhaps near-optimal.
- Strong Learner : Colloquially, a model that performs very well compared to a naive model.
We are attempting to develop a strong model when we fit a machine learning model directly on a dataset. For example, we might consider the following algorithms as techniques for fitting a strong model in the colloquial sense, where the hyperparameters of each method are tuned for the target problem:
- Logistic Regression.
- Support Vector Machine.
- k-Nearest Neighbors.
And many more methods listed in the previous section or with which you may be familiar.
Strong learning is what we seek, and we can contrast their capability with weak learners, although we can also construct strong learners from weak learners.