Cocalc 05 05 Naive Bayes Ipynb
📚 The CoCalc Library - books, templates and other resources This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! < Feature Engineering | Contents | In Depth: Linear Regression > The previous four sections have given a general overview of the concepts of machine learning.
In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification. There was an error while loading. Please reload this page. Naive Bayes classifier for Fashion MNIST data Naive Bayes is a classic machine learning algorithm which is basic, but one of the very efficient algorithm to identify the images. The simple use of Bayes theorem can help us classify the images.
This machine learning algorithm is purely statistical and uses probabality calculations. Given all the functions now we can load the data, train the data and test the data. MNIST is a very commonly used data set so we are trying to go to a harder dataset of FMNIST which has the real world applications. Fashion MNIST deals with classifying 10 fashion clothes. https://github.com/zalandoresearch/fashion-mnist is the source of the data set. Average Classification Rate: 74.7%.
The Laplace factor that yielded the best accuracy rate was interestingly 0.1. There was an error while loading. Please reload this page.
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📚 The CoCalc Library - Books, Templates And Other Resources
📚 The CoCalc Library - books, templates and other resources This notebook contains an excerpt from the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub. The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book! < Feature Engineering | Co...
In This Section And The Ones That Follow, We Will
In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification. There was an error while loading. Please reload this page. Naive Bayes classifier for Fashion MNIST data Naive Bayes is a classic machine learning algorithm which is basic, but one of the very efficient alg...
This Machine Learning Algorithm Is Purely Statistical And Uses Probabality
This machine learning algorithm is purely statistical and uses probabality calculations. Given all the functions now we can load the data, train the data and test the data. MNIST is a very commonly used data set so we are trying to go to a harder dataset of FMNIST which has the real world applications. Fashion MNIST deals with classifying 10 fashion clothes. https://github.com/zalandoresearch/fash...
The Laplace Factor That Yielded The Best Accuracy Rate Was
The Laplace factor that yielded the best accuracy rate was interestingly 0.1. There was an error while loading. Please reload this page.
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