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A classification model is a type of machine learning model used to categorize data into predefined classes or labels. These models are trained on a dataset where each instance is labeled with a specific class, allowing the model to learn the patterns and features that distinguish between different classes. Classification models are widely used in various fields, including natural language processing, image recognition, and medical diagnosis.
There are several types of classification models, each with its own strengths and weaknesses. Some of the
1. Logistic Regression: A simple yet effective model that uses a logistic function to model the probability
2. Decision Trees: A model that uses a tree-like structure to make decisions based on the features
3. Random Forests: An ensemble learning method that combines multiple decision trees to improve the accuracy
4. Support Vector Machines (SVM): A model that finds the optimal boundary or hyperplane that separates different
5. Neural Networks: A model inspired by the structure and function of the human brain. Neural networks
The performance of a classification model is typically evaluated using metrics such as accuracy, precision, recall,