Data Science, Artificial Intelligence, and Machine Learning

Genetic algorithms are increasingly popular for solving optimization, search, and machine learning problems.

IMSL implements both the simple genetic algorithm as well as more advanced variations that allow for flexibility for user-provided initial populations, stopping criteria, and phenotype encoding and decoding.      
 

Cluster analysis can be a useful technique in machine learning and artificial intelligence and can be applied across a variety of fields.

IMSL includes functions suitable for multivariate analysis, including hierarchical cluster analysis, k-means cluster analysis, principal component analysis, and factor analysis.

Regression functions are a mainstay in machine learning, and can be used for forecasting, prediction, and in determining the relationships between variables.

IMSL libraries feature functions suited to linear regression, including those used for model fitting, statistical inference and diagnostics, as well as polynomial and non-linear regression.

Read: What Is a Regression Model?

Practitioners gain valuable insight from trained decision trees that display the decision-making process. Ensemble methods, such as Random Forest and Stochastic Gradient Boosting fit many small trees to reduce noise and maximize predictive accuracy.

IMSL decision tree functions include popular tree fitting algorithms, functions for computing predicted values, printing decision trees, managing decision tree memory, and performing stochastic gradient boosting.

Support vector machines are widely used to detect and classify animals, landscape features, and objects in digital image data. As such they have been useful in many fields, including protein folding research, bioinformatics, and environmental science.

IMSL includes functions for training support vector machines, classifying unknown patterns, and freeing allocated memory when it’s no longer needed.

Read: Using Support Vector Machines 

Neural networks are used to solve a wide variety of problems in data science, includingforecasting, classification, and statistical pattern recognition – all of which are applicable for machine learning and AI applications.

IMSL features many neural network functions that make it easy to create and manage a variety of neural network types, including multilayer feedforward neural networks.

Read: What Are Neural Networks?

Naïve Bayes

A Naïve Bayes classifier can be trained to classify patterns involving thousands of attributes and applied to thousands of patterns. As a result, Naïve Bayes is a preferred algorithm for text mining and other large classification problems. IMSL includes functions for training Naïve Bayes classifiers, classifying patterns using previously trained Naïve Bayes classifiers, as well as storing and retrieving trained classifiers.