Sometimes there is a thin line between classification and regression algorithms. Many of them are used both ways, especially since a classification is just a regression model with a threshold value. If the number is above the threshold, it is classified as “true” and below as “false.”
Let’s follow the ML developers recommendation to look at the best machine learning algorithms to understand some basic ideas about how AI models work for solving classification problems, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, and Naive Bayes. We briefly outlined the theory behind each of them.
Linear regression
If you have a sequence of numbers, for example, 100, 200, 300, 400, 500, x; then linear regression will easily predict that x can subsequently be 600, 700, 800. It predicts simple linear dependencies in data and values within trends.
It is suitable for working with variables that depend linearly on another. It’s a simple and intuitive machine-learning model, easy to implement, and works quickly.
Logistic regression
Logistic regression uses the S-curve function to return the probability of the label. It is widely used when the classification problem is binary: true or false, won or lost, positive or negative.
The function generates a probability output signal. After comparing the probability with a predetermined threshold, the object is assigned the appropriate label. Here is one of the most popular frameworks to work with logistic regression.
Decision Tree
The Decision Tree builds branches of the “tree” according to a hierarchical principle, and each “branch” can be considered as a “yes-no” operator. “Branches” develop by splitting a dataset into subsets based on the essential features. The final classification takes place in the “leaves” of the decision tree.
Random Forest
As the name implies, the Random Forest algorithm is a collection of trees. This is a common ensemble method that combines the results of several predictors and decision trees. The random forest additionally uses the bagging technique, which allows each “tree” to be trained on a random sample of the original dataset and accepts the majority of votes from the “trees.” It has better generality but is less interpretable.
Support Vector Machine (SVM)
The support vector machine finds the best way to classify data based on their position relative to the boundary between the positive and negative classes. This boundary is known as a hyperplane, which maximizes the distance between data points from different categories. Like the Decision Tree and the Random Forest, the SVM can be used for classification and regression. The SVC (Support Vector Classifier) is designed to solve the classification problem.
K-Nearest Neighbour (KNN)
The K-nearest neighbors can be described as a representation of each data point in an n-dimensional space. It calculates the distance from one point to another and then assigns a label to the unobserved data based on the labels of the nearest observed data points.
Naive Bayes
This algorithm is based on Bayes’ theorem. In short, it is an approach to calculating probability based on prior knowledge and assuming that each feature is independent. The most significant advantage of this way of thinking is that although most algorithms rely on a large amount of data, this one works relatively well even with a small training data set.
Neural networks
The king of advanced machine-learning algorithms. It is suitable for prediction, recognition, and classification. It can be used almost everywhere and handles any class of tasks, but more expensive and time-consuming to implement.
A large amount of test data is required to train the net. However, despite the disadvantages, several alternatives can make the neural network the best choice. For example, cloud technologies allow using almost unlimited capacities for their operation.
Conclusion
Each algorithm has its benefits. For example, KNN is sensitive to features on the scale of differences, and multicollinearity affects the result of logistic regression. Understanding the specifics allows us to choose the appropriate model according to the data set.
Of course, these are not all machine learning algorithms; there is much more. We have only described the most basic ones that are used today.
