A classifier in artificial intelligence is a system that identifies which category an input belongs to based on its features. It’s like a decision-making tool that sorts data into defined groups, using certain criteria.
Imagine you have a basket full of different fruits—apples, oranges, and bananas. Now, your task is to sort them into different buckets. A classifier works the same way but with data—it sorts an input into different categories based on what it “looks” like.
In the context of machine learning and artificial intelligence, a Classifier is an algorithm that maps the input data to a specific category. Classification is a type of supervised learning. It predicts the class of given data points. Classes are sometimes called as targets, labels, or categories.
The classification predictive modeling is the task of approximating a mapping function (f) from input variables (X) to discrete output variables (y). The output variables are often called labels or categories. For example, spam detection in email service providers can be identified as a classification problem. This is a binary classification since there are only two classes as spam and not spam.
A classifier uses various methods to determine the most likely class. These methods can range from simple heuristic rules to complex neural networks. The most common ones are decision trees, naive bayes, linear programming, random forest, logistic regression, and support vector machines.
Decision Trees – Classification is a two-step process. Learning step is where the model is developed based on given training data. In the Prediction step, the model is used to predict the response for given data.
Naive Bayes Classifiers – are a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. They are highly scalable, requiring a number of parameters linear in the number of variables (features) in a learning problem.
Linear Classifiers – In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether an input belongs to some specific class.
Random Forests – It is a method that operates by constructing multiple decision trees during training. The decision of the majority of the trees is chosen by the random forest as the final decision.
Logistic Regression – It is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary).
All these classifiers have different strengths and weaknesses, and their performances can vary based on the dataset and problem in hand. Therefore, it’s crucial to understand these methods to choose the most suitable one.
Supervised Learning, Unsupervised Learning, SVM (Support Vector Machine), Decision Tree, Random Forest, Naive Bayes, Logistic Regression, Neural Networks, Binary Classification, Multi-Class Classification, Label, Feature, Training Data, Test Data