標(biāo)題: Titlebook: Distributed Machine Learning with PySpark; Migrating Effortless Abdelaziz Testas Book 2023 Abdelaziz Testas 2023 Python.Scalable machine le [打印本頁] 作者: 里程表 時(shí)間: 2025-3-21 16:03
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作者: 假裝是我 時(shí)間: 2025-3-21 20:36 作者: 滔滔不絕地講 時(shí)間: 2025-3-22 04:11
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作者: Infelicity 時(shí)間: 2025-3-22 06:43 作者: 玩笑 時(shí)間: 2025-3-22 12:45
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作者: 果仁 時(shí)間: 2025-3-22 14:39
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.作者: 果仁 時(shí)間: 2025-3-22 19:50 作者: Obverse 時(shí)間: 2025-3-22 21:13
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作者: cyanosis 時(shí)間: 2025-3-23 03:30 作者: Contracture 時(shí)間: 2025-3-23 07:29
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作者: 范例 時(shí)間: 2025-3-23 12:26
The British Commonwealth of Nationsl advantages, making them versatile for a wide range of tasks, from regression to classification spanning across various domains such as image recognition, natural language processing, and speech recognition, to name a few.作者: 委托 時(shí)間: 2025-3-23 15:28
https://doi.org/10.1057/9780230270749 learning is known as natural language processing (NLP), which finds uses in many business applications including speech recognition, chatbots, language translation, and email spam detection (ham or spam).作者: insincerity 時(shí)間: 2025-3-23 21:50 作者: 領(lǐng)導(dǎo)權(quán) 時(shí)間: 2025-3-23 23:22 作者: 刺耳的聲音 時(shí)間: 2025-3-24 03:46
The British Commonwealth of Nationsent is the process of making a machine learning model available for use in a production environment where it can make predictions or perform tasks based on real-world data. It involves taking a trained machine learning model and integrating it into a system or application so that it can provide pred作者: infringe 時(shí)間: 2025-3-24 06:47 作者: Inculcate 時(shí)間: 2025-3-24 12:13 作者: 忘恩負(fù)義的人 時(shí)間: 2025-3-24 18:08
The British Commonwealth And EmpireThis chapter focuses on classification, a distinct form of supervised learning. Our objective is to build, train, and evaluate a logistic regression model and then use it to predict the likelihood of diabetes.作者: excursion 時(shí)間: 2025-3-24 21:33
The British Commonwealth of NationsIn this chapter, we explore a new area of supervised learning, that of recommender systems. Even though recommender systems fall under supervised learning, they do not typically fall under either regression (Chapters .) or classification (Chapters .). They are considered a distinct area within machine learning called collaborative filtering.作者: BILK 時(shí)間: 2025-3-25 03:06
https://doi.org/10.1057/9780230270749In this chapter, we investigate the subject of hyperparameter tuning. This is a critical step in machine learning that involves finding the optimal set of hyperparameters for a given algorithm. Hyperparameters are parameters that are set before the learning process begins and affect the behavior and performance of the model.作者: DRAFT 時(shí)間: 2025-3-25 06:14 作者: 高興一回 時(shí)間: 2025-3-25 10:11 作者: Ablation 時(shí)間: 2025-3-25 13:40 作者: Abduct 時(shí)間: 2025-3-25 18:35 作者: 友好關(guān)系 時(shí)間: 2025-3-25 20:49
Hyperparameter Tuning with Scikit-Learn and PySpark,In this chapter, we investigate the subject of hyperparameter tuning. This is a critical step in machine learning that involves finding the optimal set of hyperparameters for a given algorithm. Hyperparameters are parameters that are set before the learning process begins and affect the behavior and performance of the model.作者: 未開化 時(shí)間: 2025-3-26 03:35
Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark,e steps involved in machine learning, including splitting data, model training, model evaluation, and prediction, are the same in both frameworks. Furthermore, Pandas and PySpark have similar approaches to data manipulation, which simplifies tasks like exploring data.作者: 異端 時(shí)間: 2025-3-26 07:24 作者: COUCH 時(shí)間: 2025-3-26 09:45 作者: Embolic-Stroke 時(shí)間: 2025-3-26 14:14
Neural Network Classification with Pandas, Scikit-Learn, and PySpark,l advantages, making them versatile for a wide range of tasks, from regression to classification spanning across various domains such as image recognition, natural language processing, and speech recognition, to name a few.作者: organism 時(shí)間: 2025-3-26 17:21
Natural Language Processing with Pandas, Scikit-Learn, and PySpark, learning is known as natural language processing (NLP), which finds uses in many business applications including speech recognition, chatbots, language translation, and email spam detection (ham or spam).作者: arthroplasty 時(shí)間: 2025-3-26 22:00
Pipelines with Scikit-Learn and PySpark,an automate and standardize the steps involved in the modeling workflow. This enables the building of robust and scalable models, enhances model interpretability, and facilitates the integration of additional preprocessing steps and feature engineering techniques.作者: Haphazard 時(shí)間: 2025-3-27 04:43 作者: 注意 時(shí)間: 2025-3-27 05:38 作者: Ruptured-Disk 時(shí)間: 2025-3-27 10:45 作者: OTHER 時(shí)間: 2025-3-27 14:14 作者: 泥土謙卑 時(shí)間: 2025-3-27 19:53
The British Commonwealth of Nationsl advantages, making them versatile for a wide range of tasks, from regression to classification spanning across various domains such as image recognition, natural language processing, and speech recognition, to name a few.作者: 前面 時(shí)間: 2025-3-28 01:28
https://doi.org/10.1057/9780230270749 learning is known as natural language processing (NLP), which finds uses in many business applications including speech recognition, chatbots, language translation, and email spam detection (ham or spam).作者: 和平主義 時(shí)間: 2025-3-28 03:10 作者: 一個(gè)攪動(dòng)不安 時(shí)間: 2025-3-28 09:53 作者: photopsia 時(shí)間: 2025-3-28 11:12
https://doi.org/10.1007/978-1-4842-9751-3Python; Scalable machine learning; Large-Scale machine learning; Machine Learning; PySpark; Scikit-learn; 作者: AROMA 時(shí)間: 2025-3-28 17:51 作者: 騷擾 時(shí)間: 2025-3-28 19:38
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.Un978-1-4842-9750-6978-1-4842-9751-3作者: Kidney-Failure 時(shí)間: 2025-3-29 00:47 作者: 冷淡周邊 時(shí)間: 2025-3-29 05:52
Selecting Algorithms,er, 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作者: somnambulism 時(shí)間: 2025-3-29 09:45
Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark,e steps involved in machine learning, including splitting data, model training, model evaluation, and prediction, are the same in both frameworks. Furthermore, Pandas and PySpark have similar approaches to data manipulation, which simplifies tasks like exploring data.作者: 上腭 時(shí)間: 2025-3-29 11:56
Decision Tree Regression with Pandas, Scikit-Learn, and PySpark,ion 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作者: Memorial 時(shí)間: 2025-3-29 19:25 作者: 懶惰人民 時(shí)間: 2025-3-29 19:48
Decision Tree Classification with Pandas, Scikit-Learn, and PySpark,ee classification model for predicting the species of an Iris flower based on its feature measurements. We will leverage the well-known Iris dataset, which consists of measurements of four features (sepal length, sepal width, petal length, and petal width) from three distinct species of Iris flowers作者: Feckless 時(shí)間: 2025-3-30 01:32 作者: 慢跑 時(shí)間: 2025-3-30 07:35 作者: Additive 時(shí)間: 2025-3-30 10:23 作者: MUTED 時(shí)間: 2025-3-30 15:57 作者: forbid 時(shí)間: 2025-3-30 18:18
Natural Language Processing with Pandas, Scikit-Learn, and PySpark, learning is known as natural language processing (NLP), which finds uses in many business applications including speech recognition, chatbots, language translation, and email spam detection (ham or spam).作者: 沒花的是打擾 時(shí)間: 2025-3-30 21:15
k-Means Clustering with Pandas, Scikit-Learn, and PySpark,ering is a commonly used technique in segmentation analysis to group similar observations together based on their characteristics or their proximity in the feature space. The result is a set of clusters, with each observation assigned to a specific cluster. By organizing data into clusters, we can g作者: 絕食 時(shí)間: 2025-3-31 01:24 作者: Inordinate 時(shí)間: 2025-3-31 07:44 作者: 易怒 時(shí)間: 2025-3-31 13:16
Book 2023o 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)作者: 種族被根除 時(shí)間: 2025-3-31 14:33
Decision Tree Regression with Pandas, Scikit-Learn, and PySpark, of property and the number of bedrooms, bathrooms, and stories, among others. Additionally, we will compare the performance of Pandas and PySpark in data loading and exploration tasks to better understand their similarities and differences.作者: 停止償付 時(shí)間: 2025-3-31 20:19 作者: 最小 時(shí)間: 2025-3-31 22:27 作者: coalition 時(shí)間: 2025-4-1 02:35
https://doi.org/10.1057/9780230270770 margin between data points of different classes. The hyperplane acts as a decision boundary, with one class on each side. The margin represents the perpendicular distance between the hyperplane and the closest points of each class. A larger margin indicates a better separation, while a smaller margin suggests a less optimal decision boundary.作者: Arctic 時(shí)間: 2025-4-1 09:21