Close
  Indian J Med Microbiol
 

Figure 1: (a) Decision tree algorithm: A supervised learning algorithm that models a decision tree having nodes and edges using the data sets, answers any query using a series of questions with answers usually consisting of binary value. It classifies data and predicts any new dataset based on the modeled tree. (b) K-mean algorithm: An unsupervised learning algorithm that clusters the input data based on a similarity value. It has moderate to high efficiency and is used for problems where the data are not highly dimensional. (c) Support vector machine: Classify data points by selecting the “separating hyperplane” to separate the data into two classes based on pattern difference. (d) Artificial neural networks: Simulate the behavior of a biological neuron and are organized in layers of interconnected nodes, with nodes in the input layer receiving input features and hidden layers process the input to relay through the output layer

Figure 1: (a) Decision tree algorithm: A supervised learning algorithm that models a decision tree having nodes and edges using the data sets, answers any query using a series of questions with answers usually consisting of binary value. It classifies data and predicts any new dataset based on the modeled tree. (b) K-mean algorithm: An unsupervised learning algorithm that clusters the input data based on a similarity value. It has moderate to high efficiency and is used for problems where the data are not highly dimensional. (c) Support vector machine: Classify data points by selecting the “separating hyperplane” to separate the data into two classes based on pattern difference. (d) Artificial neural networks: Simulate the behavior of a biological neuron and are organized in layers of interconnected nodes, with nodes in the input layer receiving input features and hidden layers process the input to relay through the output layer