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Titlebook: Distributed Machine Learning with PySpark; Migrating Effortless Abdelaziz Testas Book 2023 Abdelaziz Testas 2023 Python.Scalable machine le

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發(fā)表于 2025-3-21 16:03:58 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Distributed Machine Learning with PySpark
副標(biāo)題Migrating Effortless
編輯Abdelaziz Testas
視頻videohttp://file.papertrans.cn/282/281919/281919.mp4
概述Covers migrating from Pandas, Scikit-Learn to PySpark, from single-node to large-scale computing.Explains deploying ML models to production with Scikit-Learn and PySpark.Explains how to use PySpark fo
圖書封面Titlebook: Distributed Machine Learning with PySpark; Migrating Effortless Abdelaziz Testas Book 2023 Abdelaziz Testas 2023 Python.Scalable machine le
描述.Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools...Distributed Machine Learning with PySpark. offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Na?ve Bayes, and neural networks...After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines...What You Will Learn..Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systems.Un
出版日期Book 2023
關(guān)鍵詞Python; Scalable machine learning; Large-Scale machine learning; Machine Learning; PySpark; Scikit-learn;
版次1
doihttps://doi.org/10.1007/978-1-4842-9751-3
isbn_softcover978-1-4842-9750-6
isbn_ebook978-1-4842-9751-3
copyrightAbdelaziz Testas 2023
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The British Commonwealth And Empireer, testing and optimizing all of these models in each category would be incredibly cumbersome and require significant computational power. To address this challenge, this chapter introduces k-fold cross-validation, a technique that helps select the best-performing model from a range of different al
地板
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The British Commonwealth And Empireion model using the decision tree algorithm—an alternative to the multiple linear regression model we used in the previous chapter. We will use both Scikit-Learn and PySpark to train and evaluate the model and then use it to predict the sale price of houses based on several features such as the size
6#
發(fā)表于 2025-3-22 14:39:32 | 只看該作者
https://doi.org/10.1057/9780230270770el using the same housing dataset we used for decision tree and random forest regression in the preceding chapters. This way, we can have a better idea about which tree type performs better by comparing their performance metrics.
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https://doi.org/10.1057/9780230270770aluating a random forest classifier to classify the species of an Iris flower using the same dataset employed in the previous chapter. Previously, we emphasized that decision trees are powerful machine learning algorithms adept at classification tasks. Nonetheless, they can be susceptible to overfit
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發(fā)表于 2025-3-23 03:30:50 | 只看該作者
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發(fā)表于 2025-3-23 07:29:03 | 只看該作者
https://doi.org/10.1057/9780230270770chine learning technique widely recognized for its simplicity and ease of implementation in classification tasks. It is computationally efficient, making it suitable for large datasets and real-time applications. It can work well with relatively small datasets because it relies on simple probability
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