- Classification in machine learning is a supervised learning technique aimed at predicting the category or class of an instance based on its features.
- Classification algorithms are crucial in machine learning for organising and interpreting complex datasets. They enable the categorisation of data into specific classes or labels, facilitating automated decision-making and pattern recognition.
1. Logistic Regression
Logistic regression is a classification algorithm used to estimate discrete values, typically binary, such as 0 and 1, or yes and no. It predicts the probability of an instance belonging to a particular class, making it essential for binary classification problems like spam detection or diagnosing diseases. By modelling the relationship between input features and the probability of a certain outcome, logistic regression helps determine the likelihood of a specific class, which is then used to classify new instances.
2. Decision Tree
Decision trees are versatile and straightforward techniques used for both classification and regression tasks. They work by recursively splitting the dataset into subgroups based on key criteria, resulting in a tree-like structure where decisions made at each node lead to different branches, ultimately ending in leaf nodes that represent final outcomes. Their simplicity and clarity make them particularly useful for decision-making processes, as they are easy to understand and visualise. However, decision trees are prone to overfitting, where the model becomes too tailored to the training data and performs poorly on new data. To address this, pruning—removing sections of the tree that offer little predictive power—can be employed to improve the model’s generalisability. The tree-like model can effectively represent decisions and their potential consequences, including chance event outcomes, resource costs, and utility.
Also read: 3 differences between machine learning and deep learning for neural networks
3. Random Forest
Random Forest is an ensemble learning technique that improves prediction accuracy and reduces overfitting by combining the results of multiple decision trees. It creates numerous trees using random subsets of data and features, then aggregates their predictions. This approach is effective for both classification and regression tasks, particularly with high-dimensional data, offering robust predictions and resistance to overfitting.
4. Support Vector Machine (SVM)
Support Vector Machines (SVM) are powerful algorithms for classification and regression tasks. They work by finding the optimal hyperplane that best separates data into classes while maximising the margin between them. SVMs perform well in high-dimensional spaces and can handle nonlinear relationships between features using kernel methods, making them highly accurate for complex datasets.
Also read: What is classification in neural networks and why is it important?
5. Naive Bayes
Naive Bayes is a probabilistic classification algorithm commonly used for text categorisation and spam filtering. It relies on Bayes’ theorem to calculate the likelihood of a class based on conditional probabilities of features. Despite its simplicity and the “naive” assumption that features are independent of each other, Naive Bayes performs well in practice, especially with high-dimensional datasets. It is effective because it quickly processes data and often yields good results even with the independence assumption.
6. K-Nearest Neighbors (KNN)
K-Nearest Neighbors (KNN) is a non-parametric, instance-based learning algorithm used for both classification and regression. It classifies new data points by considering the majority class among its k-nearest neighbors, using a similarity measure like distance. KNN is versatile, performing well on tasks with uneven decision boundaries, and is effective in handling non-linear data. Its simplicity and adaptability make it popular in recommendation systems, anomaly detection, and pattern recognition.
