Classification in Machine Learning
In the field of machine learning, classification refers to the process of identifying the category an input belongs to. This is achieved by using a classification model that has been developed through advanced machine learning techniques.
Example: A classic instance of classification is determining whether an email is spam or not.
An object or element under observation in this context is termed an (instance).
The algorithm responsible for this process is known as a classifier.
This classifier scrutinizes each instance and categorizes it accordingly.
Note: Binary classification refers to scenarios with just two categories, such as spam or no spam. When dealing with more than two categories, the term multiclass classification (multiclass classification or multinomial) is used.
Classification Algorithms
Classification models are generally created through supervised learning techniques ( Supervised ML ), utilizing a training dataset with accurately classified examples.
Yet, unsupervised learning methods ( Unsupervised ML ) can also be employed for classification.
Example: Clustering algorithms, which organize data based on similarity or distance, exemplify classification via unsupervised learning.
Classification algorithms can be broadly categorized into linear and nonlinear types.
- Linear Classification
Here, the classes are separated by a linear boundary, such as a straight line or a plane. - Nonlinear Classification
In this approach, the class boundaries are curved.
Note: Linear classification algorithms often struggle with underfitting, trading complexity for speed and simplicity. On the other hand, nonlinear classification algorithms can be prone to overfitting, where they become too tailored to the training data.
