Machine learning behind classification tasks in various engineering and science domains
| Autor: | Tilottama Goswami |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
business.industry Computer science NumPy Decision tree Python (programming language) Machine learning computer.software_genre Support vector machine Multiclass classification Naive Bayes classifier Statistical classification Artificial intelligence business computer computer.programming_language |
| DOI: | 10.1016/b978-0-12-819443-0.00016-7 |
| Popis: | Prediction, forecast, and prognosis about the future is an interesting analysis and one of the most popularly used applications in various domains of science and technology, management science, medical science, agricultural science, and many more applied fields. This task of prediction can be automated using classification techniques, a part of supervised machine learning (ML) algorithms. The classification technique is applied to classify true/false outcome or multiple classes of any prediction. Logistic regression, Naive Bayes, k-nearest neighbor, decision trees, support vector machines, and artificial neural network are some of the popularly used classification algorithms in various domains. Depending on the case studies from various domains, clean and considerable amount of data is one of the basic requirements for class prediction. Some of the case studies from various domains are discussed in this chapter. The datasets, classification programs written in Python are provided for each case study. The algorithms to be used vary depending upon various factors such as binary or multiclass classification, multivariate or temporal datasets, size of training data, fast or accurate prediction. The ML is an upcoming field, the Python language is very popular for building software, with its rich set of libraries for efficient data management such as pandas, scientific numerical computation using NumPy, ML algorithms based on scikit-learn, visualization tools using Matplotlib, neural network algorithms using Keras, etc. The author in this chapter will showcase various ML tasks with case studies using Python programing language so that the novice readers in this field can start their learning curve smoothly. |
| Databáze: | OpenAIRE |
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