Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 16

A subset of AI focuses on algorithm development by learning from experience and helps in the improvement of decision making with greater accuracy. The categories and the corresponding tasks are shown in Figure 1.3. Supervised, unsupervised, and reinforcement are the three main learning paradigms. Supervised is the most prevalent training paradigm for developing ML models for both classification and regression tasks [27]. It finds the relationship between the input and target variables. Some of the supervised learning algorithms are support vector machine (SVM), logistic regression, Decision Tree (DT), random forest, and so on. Unsupervised learning is often used for clustering and segmentation tasks. This method does not require any target variable to group the input data sets. Some of the examples are K-means, hierarchical, density, grid clustering, and so on. Reinforcement learning corresponds to responding to the environment and deciding the right action to complete the assignment with maximum reward in a given application. It finds its applications in a real-time environment.

Figure 1.3 Types of machine learning.

Figure 1.4 Generic methodology in building a model using machine learning algorithms.

In ML, training is performed with a huge amount of data to get accurate decisions or predictions. The general steps involved in building an ML model are shown in Figure 1.4.