What is classification?
Classification is the process of predicting the class of a given set of data points. Classes are sometimes called targets/labels or categories. It belongs to a category of supervised learning, where the targets are also provided with the input data.
Classification algorithms in supervised machine learning use input training data to predict the likelihood that subsequent data will fall into one of the predetermined categories. Using a classification algorithm, we can perform things like sentiment analysis to categorize unstructured tests by the polarity of opinion (positive, negative, neutral, and beyond).
What are the types of classification algorithms?
The types of classification algorithms are:
- Logistic Regression– It is a binary classification algorithm that gives out the probability for something to be true or false.
- Decision Tree– It is a tree-based classifier, where each node splits into its children based on a condition.
- Random Forest– It is an ensemble technique, which is a collection of multiple decision trees.
- Naïve Bayes– It is a probabilistic classifier that is built on the principle of the Bayes theorem. Naïve Bayes classifier assumes that one particular feature in a class is unrelated to any other feature and that is why it is known as naïve.