ml1.2

• Classification


  The task of "classifying things" into sub-categories, by a machine
• Classification


  • Part of supervised machine learning in which we put labeled data for training
• Classification


  The problem of identifying to which of a set of categories (subpopulations), a new observation belongs, on the basis of a training set of data containing observations and whose categories membership is known
• Classification


  A process of categorizing data or objects into predefined classes or categories based on their features or attributes
• Classification


  A type of supervised learning technique where an algorithm is trained on a labeled dataset to predict the class or category of new, unseen data
• Objective of classification


  To build a model that can accurately assign a label or category to a new observation based on its features
• Example of classification


  • Classifying images as either dogs or cats based on features like color, texture, and shape
• Binary classification


  The goal is to classify the input into one of two classes or categories
• Example of binary classification


  • Determining whether a person has a certain disease or not based on their health conditions
• Multiclass classification


  The goal is to classify the input into one of several classes or categories
• Example of multiclass classification


  • Determining which species a flower belongs to based on data about different species
• Types of classification algorithms

  • Linear classifiers
  • Non-linear classifiers
• Linear classifiers


  • Create a linear decision boundary between classes, are simple and computationally efficient
• Examples of linear classifiers


  • Logistic regression
  • Support Vector Machines with linear kernel
  • Single-layer Perceptron
  • Stochastic Gradient Descent (SGD) Classifier
• Non-linear classifiers


  • Create a non-linear decision boundary between classes, can capture more complex relationships between input features and target variable
• Examples of non-linear classifiers

  • K-Nearest Neighbours
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Ensemble learning classifiers: Random Forests, AdaBoost, Bagging Classifier, Voting Classifier, ExtraTrees Classifier
  • Multi-layer Artificial Neural Networks
• Lazy learners


  Also known as instance-based learners, do not learn a model during training but simply store the training data and use it to classify new instances at prediction time, fast at prediction but less effective in high-dimensional spaces or large training datasets
• Examples of lazy learners


  • k-nearest neighbors, case-based reasoning
• Eager learners


  Also known as model-based learners, learn a model from the training data during the training phase and use this model to classify new instances at prediction time, more effective in high-dimensional spaces with large training datasets
• Examples of eager learners

  • Decision trees, random forests, support vector machines
• In this lecture, learners are taught about: What is Classification, Types of Classification, Types of classification algorithms, Type of Learners in Classifications Algorithm
• Homework questions: Choose a specific industry and discuss how classification algorithms are employed for solving problems in that industry, Define classification and explain its significance in machine learning, Provide examples of real-world applications where classification is used