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Classification

Machine learning classification is a type of supervised learning where an algorithm learns to categorize data into predefined classes. The model is trained on labeled data, meaning each input has a corresponding output label.

1. Types of Classification​

Binary Classification​

  • The model predicts one of two possible classes.
  • Example: Email spam detection (Spam or Not Spam).

Multiclass Classification​

  • The model predicts one class out of three or more possible classes.
  • Example: Classifying images of animals (Dog, Cat, Bird).

Multilabel Classification​

  • The model assigns multiple labels to a single instance.
  • Example: A movie can be classified as both "Action" and "Comedy."

2. Common Classification Algorithms​

Linear Models​

  • Logistic Regression: A statistical method used for binary classification.
  • Linear Discriminant Analysis (LDA): Used when classes are well separated.

Non-Linear Models​

  • Decision Trees: Simple and interpretable but prone to overfitting.
  • Random Forest: An ensemble of decision trees, reducing overfitting.
  • Support Vector Machine (SVM): Finds the best boundary between classes.
  • K-Nearest Neighbors (KNN): Assigns labels based on the closest neighbors.

Neural Networks​

  • Deep Learning Models: Used for complex classification tasks like image and speech recognition.

3. Steps in Classification​

  1. Data Collection – Gather labeled data.
  2. Data Preprocessing – Handle missing values, normalize data, etc.
  3. Feature Engineering – Select important features for classification.
  4. Model Selection – Choose a suitable classification algorithm.
  5. Training & Testing – Train the model and evaluate performance.
  6. Hyperparameter Tuning – Optimize the model for better accuracy.
  7. Deployment & Monitoring – Deploy the model and monitor real-world performance.

4. Evaluation Metrics​

  • Accuracy: Overall correctness of predictions.
  • Precision & Recall: Useful for imbalanced datasets.
  • F1-Score: Harmonic mean of precision and recall.
  • ROC-AUC Curve: Evaluates classification thresholds.
Citation

Getting started with Classification​

Classification teaches a machine to sort things into categories. It learns by looking at examples with labels (like emails marked “spam” or “not spam”). After learning, it can decide which category new items belong to, like identifying if a new email is spam or not. For example a classification model might be trained on dataset of images labeled as either dogs or cats and it can be used to predict the class of new and unseen images as dogs or cats based on their features such as color, texture and shape.

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https://www.geeksforgeeks.org/getting-started-with-classification/