Classification in Machine learning

Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data. In classification, the model is fully trained using the training data, and then it is evaluated on test data before being used to perform prediction on new unseen data.

For instance, an algorithm can learn to predict whether a given email is spam or ham (no spam), as illustrated .

Before diving into the classification concept, we will first understand the difference between the two types of learners in classification: lazy and eager learners. Then we will clarify the misconception between classification and regression. 

Lazy Learners Vs. Eager Learners

There are two types of learners in machine learning classification: lazy and eager learners. 

Eager learners are machine learning algorithms that first build a model from the training dataset before making any prediction on future datasets. They spend more time during the training process because of their eagerness to have a better generalization during the training from learning the weights, but they require less time to make predictions. 

Most machine learning algorithms are eager learners, and below are some examples: 

• Logistic Regression. 
• Support Vector Machine. 
• Decision Trees. 
• Artificial Neural Networks. 

Lazy learners or instance-based learners, on the other hand, do not create any model immediately from the training data, and this is where the lazy aspect comes from. They just memorize the training data, and each time there is a need to make a prediction, they search for the nearest neighbor from the whole training data, which makes them very slow during prediction. Some examples of this kind are: 

• K-Nearest Neighbor. 
• Case-based reasoning. 

However, some algorithms, such as BallTrees and KDTrees, can be used to improve the prediction latency. 

Machine Learning Classification Vs. Regression

There are four main categories of Machine Learning algorithms: supervised, unsupervised, semi-supervised, and reinforcement learning. 

Even though classification and regression are both from the category of supervised learning, they are not the same. 

• The prediction task is a classification when the target variable is discrete. An application is the identification of the underlying sentiment of a piece of text. 
• The prediction task is a regression when the target variable is continuous. An example can be the prediction of the salary of a person given their education degree, previous work experience, geographical location, and level of seniority.

Examples of Machine Learning Classification in Real Life 

Supervised Machine Learning Classification has different applications in multiple domains of our day-to-day life. Below are some examples. 

Healthcare 

Training a machine learning model on historical patient data can help healthcare specialists accurately analyze their diagnoses:

• During the COVID-19 pandemic, machine learning models were implemented to efficiently predict whether a person had COVID-19 or not. 
• Researchers can use machine learning models to predict new diseases that are more likely to emerge in the future. 

Education 

Education is one of the domains dealing with the most textual, video, and audio data. This unstructured information can be analyzed with the help of Natural Language technologies to perform different tasks such as:

• The classification of documents per category. 
• Automatic identification of the underlying language of students' documents during their application. 
• Analysis of students’ feedback sentiments about a Professor.  

Transportation

Transportation is the key component of many countries' economic development. As a result, industries are using machine and deep learning models: 

• To predict which geographical location will have a rise in traffic volume. 
• Predict potential issues that may occur in specific locations due to weather conditions. 

Sustainable agriculture

Agriculture is one of the most valuable pillars of human survival. Introducing sustainability can help improve farmers' productivity at a different level without damaging the environment:

• By using classification models to predict which type of land is suitable for a given type of seed.
• Predict the weather to help them take proper preventive measures. 

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