Classification

This is a Supervised Learning approach to predicting discrete values.

It involves categorizing input data into a set of pre-defined classes or categories. In classification, an algorithm is trained on a labeled dataset, where the input data is associated with a known output or target variable. The algorithm then uses this training data to build a model that can predict the class label of new, unlabeled data.

Classification is used in a wide range of applications, including image recognition, spam filtering, credit scoring, sentiment analysis, and medical diagnosis, among others. Some commonly used classification algorithms in data science include Decision Tree, Logistic Regression, Support Vector Machine, and Artificial Neural Network.

Evaluation of classification models is typically based on metrics such as Accuracy, Precision, Recall, and F1 Score, which measure the model's ability to correctly predict the class labels of both positive and negative examples. Feature selection and engineering are also important steps in classification, as they can improve the accuracy and interpretability of the model by selecting the most relevant features or transforming the input data into a more informative representation.

Classification

META