Getting started with Classification

Classification is a supervised machine learning technique used to predict labels or categories based on input data. The goal is to assign each data point to a predefined class, such as spam vs. non-spam emails or diseased vs. healthy patients.

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 colour, texture or shape.

Explaining classification in ML, the horizontal axis represents the combined values of colour and texture features. The vertical axis represents the combined values of shape and size features.

• Each coloured dot in the plot represents an individual image, with the colour indicating whether the model predicts the image to be a dog or a cat.
• The shaded areas in the plot show the decision boundary, which is the line or region that the model uses to decide which category (dog or cat) an image belongs to. The model classifies images on one side of the boundary as dogs and on the other side as cats, based on their features.

Types of Classification

When we talk about classification in machine learning, we’re talking about the process of sorting data into categories based on specific features or characteristics. There are different types of classification problems depending on how many categories (or classes) we are working with and how they are organized.

1. Binary Classification

This is the simplest kind of classification. In binary classification, the goal is to sort the data into two distinct categories. Think of it like a simple choice between two options. Imagine a system that sorts emails into either spam or not spam. It works by looking at different features of the email like certain keywords or sender details and decides whether it’s spam or not. It only chooses between these two options.

2. Multiclass Classification

Here, instead of just two categories, the data needs to be sorted into more than two categories. The model picks the one that best matches the input. Think of an image recognition system that sorts pictures of animals into categories like cat, dog and bird.

3. Multi-Label Classification

In multi-label classification single piece of data can belong to multiple categories at once. Unlike multiclass classification where each data point belongs to only one class, multi-label classification allows datapoints to belong to multiple classes. A movie recommendation system could tag a movie as both action and comedy. The system checks various features (like movie plot, actors or genre tags) and assigns multiple labels to a single piece of data, rather than just one.

Multilabel classification is relevant in specific use cases, but not as crucial for a starting overview of classification.

How does Classification in Machine Learning Work?

Classification involves training a model using a labeled dataset where each input is paired with its correct output label. The model learns patterns and relationships in the data, so it can later predict labels for new, unseen inputs. Here's how it works:

1. Data Collection: We start with a dataset where each item is labeled with the correct class.
2. Feature Extraction: The system identifies features like color, shape or texture that help distinguish one class from another. These features are what the model uses to make predictions.
3. Model Training: Classification algorithm uses the labeled data to learn how to map the features to the correct class. It looks for patterns and relationships in the data.
4. Model Evaluation: Once the model is trained, it's tested on new, unseen data to check how accurately it can classify the items.
5. Prediction: After being trained and evaluated, the model can be used to predict the class of new data based on the features it has learned.
6. Model Evaluation: Evaluating a classification model is a key step in machine learning. It helps us check how well the model performs and how good it is at handling new, unseen data. Depending on the problem and needs we can use different metrics to measure its performance.

If the quality metric is not satisfactory, the ML algorithm or hyperparameters can be adjusted and the model is retrained. This iterative process continues until a satisfactory performance is achieved.

Classification Algorithms

Now, for implementation of any classification model it is essential to understand Logistic Regression, which is one of the most fundamental and widely used algorithms in machine learning for classification tasks. There are various types of classifiers algorithms. Some of them are : 

1. Linear Classifiers: Linear classifier models create a linear decision boundary between classes. They are simple and computationally efficient. Some of the linear classification models are as follows: 

• Logistic Regression
• Support Vector Machines having kernel = 'linear'
• Single-layer Perceptron
• Stochastic Gradient Descent (SGD) Classifier

2. Non-linear Classifiers: Non-linear models create a non-linear decision boundary between classes. They can capture more complex relationships between input features and target variable. Some of the non-linear classification models are as follows:

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

Examples of Machine Learning Classification in Real Life

Classification algorithms are widely used in many real-world applications across various domains, including:

• Email spam filtering: Filters our emails into spam or not spam based on email content.
• Credit risk assessment: Algorithms predict whether a loan applicant is likely to default by analyzing factors such as credit score, income and loan history. This helps banks make informed lending decisions and minimize financial risk.
• Medical diagnosis: Machine learning models classify whether a patient has a certain condition like cancer or diabetes based on medical data such as test results, symptoms and patient history. This aids doctors in making quicker, more accurate diagnoses, improving patient care.
• Image classification : Applied in fields such as facial recognition, autonomous driving and medical imaging.
• Sentiment analysis: Determining whether the sentiment of a piece of text is positive, negative or neutral. Businesses use this to understand customer opinions, helping to improve products and services.
• Fraud detection : Algorithms detect fraudulent activities by analyzing transaction patterns and identifying anomalies crucial in protecting against credit card fraud and other financial crimes.
• Recommendation systems : Used to recommend products or content based on past user behavior, such as suggesting movies on Netflix or products on Amazon. This personalization boosts user satisfaction and sales for businesses.