Now, Machine Learning (ML) is one of the most important areas in Artificial Intelligence. It is a field that enables computer programs to learn from data without being overtly programmed by humans. There are several different categories of algorithms in Machine Learning, all of which help achieve the best results. We will discuss some of these categories of algorithms in this article.

Explanatory algorithms

One of the biggest challenges in machine learning is truly understanding how different models arrive at their final predictions. We often know the “what” but can rarely explain the “why”.

Explanatory algorithms identify variables that affect the outcome variable of interest. Such algorithms enable clear understanding of the relationship among variables in the model and not solely for outcome prediction.

There exist a couple of algorithms that help in deepening the understanding of the relationship between the independent variables and the dependent variable of a given model.

Pattern mining algorithms

Pattern mining algorithms are data mining techniques for discovering patterns and relationships in a given dataset. These algorithms could be applied to a host of other things: from customer buying patterns in a retail setting to common sequences of user behaviors on websites/ apps, through the relationship among different variables in a scientific study.

Pattern mining algorithms typically consider large datasets to discover the repetition of patterns or association between variables. After the identification process, such patterns can be used in the prediction of future trends or outcomes, or even in understanding the underlying relationships between data variables.

Team learning

Ensemble algorithms are a kind of machine learning in which several models make predictions, and it is the average or weighted average of the different predictions—improved prediction over single models. Several reasons exist as to why team algorithms can outperform traditional machine learning algorithms.

Variety will have the ensemble algorithms catching more patterns in data by combining predictions from several models.

The reasons are that team algorithms are less prone to noise and outliers in the dataset, hence giving more stable and reliable predictions;

Secondly, ensemble algorithms reduce overfitting since they only capture an average of multiple models’ predictions.

Higher Accuracy: Algorithms developed for teams have shown to predominantly perform better, in most settings, than classical machine learning algorithms.

Clustering

Clustering algorithms are unsupervised learning tasks and are used to group data into “clusters”. Unlike supervised learning where the target variable is known, in clustering there is no target variable.

This is a good technique for discovering any natural patterns or trends in data. It is also often used at the data mining phase, where a better understanding of the data might be helpful. In addition, clustering can allow for the segmentation of a dataset into different segments based on a variety of variables. This is commonly applied in the segmentation of customers or users.

Time series algorithms

Time series algorithms are techniques used in analyzing time-dependent data. The algorithms consider time relations between the different data points of a series, so the study is very instrumental in predicting future values.

Time series algorithms are applied in a myriad of business applications, like product demand prediction, sales forecasting, and the trend in customer behaviors. They have also been applied to detect anomalies or changes of trends in data.

Similarity algorithms

They measure the similarity between pairs of records, nodes, or data points or text. The algorithms are normally based on the distance among two data points, such as Euclidean distance, or textual similarity, like the Levenshtein algorithm.

Their applicability domains are very wide, but it is in recommendations where they become really useful; applied to find similar items or recommending alike content to the end-users.

The features of machine learning are endless; many algorithms exist that AIs use to become smart and perform tasks in a wide array previously impossible for machines.