Classification methods
Machine learning algorithms are useful in many real-world applications, for example, if someone is interested in making accurate predictions about the climate or in the diagnosis of a disease. The learning is usually based on some known behavior or observations. This means that machine learning is about learning to improve on something in the future based on the experience or observations of the past.
Machine learning algorithms are broadly categorized as supervised learning, unsupervised learning, reinforced learning, and deep learning. The supervised learning method of classification (where the test data is labeled) is similar to a teacher who supervises different classes. Supervised learning relies on the algorithm to learn from data when we specify a target variable. Building an accurate classifier requires the following features:
- A good set of training examples
- A reasonably good performance on the training set
- A classifier method that is closely related to prior...
