Data Science Glossary: What is Classification

Classification is a supervised machine learning problem. Classification deals with categorizing a data point based on its similarity to other data points.

You take a set of data where every item already has a category and look at common traits between each item. You then use those common traits as a guide for what category the new item might have. Classification can be performed on structured or unstructured data. Classification is a technique where we categorize data into a given number of classes. The main goal of a classification problem is to identify the category/class to which a new data will fall under. Few of the terminologies encountered in machine learning – classification:

• Classifier: An algorithm that maps the input data to a specific category.
• Classification model: A classification model tries to draw some conclusion from the input values given for training. It will predict the class labels/categories for the new data.
• Feature: A feature is an individual measurable property of a phenomenon being observed.
• Binary Classification: Classification task with two possible outcomes. Eg: Gender classification (Male / Female)
• Multi-class classification: Classification with more than two classes. In multi class classification each sample is assigned to one and only one target label. Eg: An animal can be cat or dog but not both at the same time
• Multi-label classification: Classification task where each sample is mapped to a set of target labels (more than one class). Eg: A news article can be about sports, a person, and location at the same time.

The following are the steps involved in building a classification model:

• Initialize the classifier to be used.
• Train the classifier: All classifiers in scikit-learn uses a fit(X, y) method to fit the model(training) for the given train data X and train label y.
• Predict the target: Given an unlabeled observation X, the predict(X) returns the predicted label y.
• Evaluate the classifier model