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The prevalence of machine learning has been increasing tremendously in recent years due to the high demand and advancements in technology. The potential of machine learning to create value out of data has made it appealing for businesses in many different industries. Here are the different types of machine learning algorithms.
The Naïve Bayes classifier is based on Bayes' theorem and classifies every value as independent of any other value. It allows us to predict a class/category, based on a given set of features, using probability. Despite its simplicity, the classifier does surprisingly well and is often used due to the fact it outperforms more sophisticated classification methods.
The K Means Clustering algorithm is a type of unsupervised learning, which is used to categorize unlabeled data, i.e., data without defined categories or groups. The algorithm works by finding groups within the data, with the number of groups represented by the variable K. It then works iteratively to assign each data point to one of K groups based on the features provided.
Support Vector Machine algorithms are supervised learning models that analyze data used for classification and regression analysis. They essentially filter data into categories, which is achieved by providing a set of training examples, each set marked as belonging to one or the other of the two categories. The algorithm then works to build a model that assigns new values to one category or the other.
Linear regression is the most basic type of regression. Simple linear regression allows us to understand the relationships between two continuous variables.
Logistic regression focuses on estimating the probability of an event occurring based on the previous data provided. It is used to cover a binary dependent variable, that is where only two values, 0 and 1, represent outcomes.
An artificial neural network (ANN) comprises 'units' arranged in a series of layers, each of which connects to layers on either side. ANNs are inspired by biological systems, such as the brain, and how they process information. ANNs are essentially a large number of interconnected processing elements, working in unison to solve specific problems. ANNs also learn by example and through experience, and they are extremely useful for modeling non-linear relationships in high-dimensional data or where the relationship amongst the input variables is difficult to understand.
A decision tree is a flow-chart-like tree structure that uses a branching method to illustrate every possible outcome of a decision. Each node within the tree represents a test on a specific variable – and each branch is the outcome of that test.
