Classification In Data Science and Machine Learning
Many types of scenarios are observed in machine learning. Some problems are related to predicting a numerical value of some attributes using the other attributes and past data. These types of problems are related to regression. Some problems can be related to determining the specific group or class of the particular data items using the past data classes and data attributes. These types of problems are related to classification problems. Consider an example of the medical data set. Let us have the data set in which there are symptoms of different diseases, and in the class attribute, related diseases are given. Using the machine learning algorithm, we have to find out the class values for the new data. These types of scenarios and problems are called the classification problems in machine learning and data science.
Classification And Its Data Types:
In classification, machine learning and data science algorithms assign a specific label to the input data set. The classification has many machine learning and data science algorithms. There are several types of machine learning and data science classification problems and algorithms. Here are so of the classification types mostly used in machine learning and data sciences.
- Predictive Classification
- Binary Classification
- Multi-Class Classification
- Multi-Label Classification
- Imbalance Classification
In this type of classification, the machine learning algorithm predicts the class of the input data item. One of the famous examples of predictive classification is the classification of Ham and Spam Email messages classification. In this type of classification, the algorithm predicts the class label given in the data input. The model predicts the class label using the past data as training.
In this type of classification, the labels of the class are always two. The machine learning classification algorithms predict the data set class using the past data as an input. In this type of classification, the class variable can be yes or no, 0 or 1. But it is not compulsory to be 0 or 1 and yes or no. Specifically, the classes in the data set are always two. For example, Ham and Spam classification can also be considered the binary classification problem.
In this classification, the number of classes in the data set can be more than two. The machine learning classification algorithm predicts the data set class using the past data as an input. For example, face classification problems and plant disease classification problems can be considered as multi-class classification problems.
In these types of problems, there can be multiple labels of the class in the data set. The Machine learning algorithm predicts the multiple class labels of the data set by using the past data as training. The classification or prediction of multiple objects in a photo or image using the classification model is an example of multi-label classification.
We have discussed one of the most used techniques in machine learning for solving various problems. For more articles related to data science and machine learning, please keep visiting our platform.
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