What are classification problems?

Classification problems are the problems in which an object is to be classified in one of the n classes based on the similarity index of its features with that of each class. By classes, we mean a collection of similar objects. The objects are said to be similar on the basis of matching features, e.g., color, shape, size, etc. The classes are identified on the basis of their unique labels.

Classification problems example

Consider an example of three containers. Containers 1, 2, and 3 have red, blue, and green balls, respectively. Let’s say we get a new ball and are asked to place it in the container it belongs to. The problem here is a classification problem, as we have to classify which container the ball belongs to. We will place the ball in a container depending on its color. Let’s say the ball is red; it will be placed in a container already containing red balls.

Classification problems in deep learning

In deep learning, classification problems are solved by training classification models. The classification models are trained by providing objects and their labels. The models learn and identify similar features of objects in a class. After training, the model is tested on a separate dataset that it was not trained on. For testing, only the object to classify is given without its label. The classification model predicts the label of the object. The accuracy of the model is determined on the basis of correctly predicted labels.

Types of classification problems

Classification problems can be broadly categorized into the following types based on the number of classes and nature of the dataset:

• Binary Classification: The classification problems in which the number of classes is 2. For example, determining whether a statement is sarcastic or not.

• Multi-Class Classification: The classification problems in which the number of classes is more than 2. For example, categorizing text into various topics for better readability.

• Multi-Label Classification: The classification problems in which an object can belong to multiple classes. For example, identifying multiple objects in an image.

• Imbalanced Classification: The classification problems in which the number of objects in the classes is imbalanced. For example, a dataset with 90% of the samples labeled as one class and only 10% as another.

Examples of classification problems

Classification plays a significant role in our daily lives, helping to address a variety of daily problems. Here are some common real-world examples of classification problems.

• Spam detection: Classify emails as spam or not spam based on their content and characteristics.

• Image classification: Identify objects or entities in images, such as recognizing digits in handwritten digits recognition or classifying animals in photos.

• Medical diagnosis: Classify medical conditions as normal or abnormal based on patient data and diagnostic tests.

• Customer churn prediction: Based on historical usage patterns and customer behavior, predict whether a customer is likely to churn (leave) a subscription service.

• Sentiment analysis: Determine the sentiment expressed in a piece of text (positive, negative, or neutral).

Algorithms for classification problems

Popular algorithms for classification problems include:

• Logistic regression: A statistical model that predicts probabilities for binary outcomes based on input features.

• Decision trees: A tree-like structure that splits data based on feature values to make predictions.

• Support vector machines (SVM): A model that finds the best boundary (hyperplane) to separate data points into classes.

• K-nearest neighbors: A simple algorithm that classifies data points based on the majority class of their nearest neighbors.

• Artificial neural networks: A network of interconnected nodes (neurons) inspired by the human brain, used to learn patterns for classification.

The choice of algorithm depends on the nature of the data and the specific requirements of the problem at hand.

Code example

Let’s create a simple Python example for a classification problem using the popular scikit-learn library. In this example, we’ll use the Iris dataset, a commonly used dataset for classification. We’ll train a support vector machine (SVM) classifier to predict the species of iris flowers based on their sepal length and width.

Code explanation

• Line 8–10: We load the Iris dataset, which contains features (sepal length, sepal width, petal length, petal width) and target labels (species: setosa, versicolor, virginica).

• Line 13: The dataset is split into training and testing sets using train_test_split.

• Line 16: We initialize an SVM classifier with a linear kernel.

• Line 19: The classifier is trained on the training data using the fit method.

• Line 22: Predictions are made on the test data using the predict method.

• Line 30: The accuracy and a classification report are printed to evaluate the performance of the classifier.

Output explanation

The output shows that the Support Vector Machine (SVM) classifier achieved perfect accuracy (1.00) in predicting the species of Iris flowers using features like sepal length, sepal width, petal length, and petal width. The classification report highlights precision, recall, and F1-scores of 1.00 for all three classes (Iris-setosa, Iris-versicolor, and Iris-virginica), indicating no false positives or false negatives. This indicates that the linear kernel of the SVM worked effectively to separate the species based on their feature distributions in the dataset.