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Scalable and Flexible Gradient Boosting
1,993
Unternehmen
Wir haben Daten zu 1,993 Unternehmen, die XGBoost verwenden. Unsere XGBoost Kundenliste steht zum Download bereit und ist mit wichtigen Unternehmensspezifika angereichert, darunter Branchenklassifikation, Organisationsgröße, geografische Lage, Finanzierungsrunden und Umsatzzahlen, unter anderem.
Unternehmen | Land | Branche | Mitarbeiter | Umsatz |
---|---|---|---|---|
JPMorgan Chase Bank, N.A. | Vereinigte Staaten | Financial Services | 76K | $135M |
![]() Affirm | Vereinigte Staaten | Financial Services | 2.6K | $1.2B |
EY | Vereinigtes Königreich | Professional Services | 357K | $45B |
Cash App | Vereinigte Staaten | Technology, Information And Internet | 3.9K | $6B |
![]() Instacart | Vereinigte Staaten | Software Development | 20K | $1.5B |
Nexient | Vereinigte Staaten | It Services And It Consulting | 630 | $90M |
Yelp | Vereinigte Staaten | Software Development | 7.9K | $1.1B |
![]() Clearcover | Vereinigte Staaten | Insurance | 440 | $13M |
Tesco | Vereinigtes Königreich | Retail | 97K | |
BMO Financial Group | Kanada | Financial Services | 52K | $25B |
Afterpay | Australien | Retail | 980 | $696M |
Intact | Kanada | Insurance | 18K |
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Nutzungsstatistiken für Technologie und Marktanteil
Sie können diese Daten an Ihre Bedürfnisse anpassen, indem Sie nach Geografie, Branche, Unternehmensgröße, Umsatz, Technologienutzung, Jobpositionen und mehr filtern. Sie können die Daten im Excel- oder CSV-Format herunterladen.
Sie können Alarme für diese Daten erhalten. Sie können beginnen, indem Sie die Technologie auswählen, die Sie interessiert, und dann erhalten Sie Alarme in Ihrem Posteingang, wenn es neue Unternehmen gibt, die diese Technologie verwenden.
Sie können seine Daten in eine Excel-Datei exportieren, die in Ihr CRM importiert werden kann. Sie können die Daten auch an eine API exportieren.
XGBoost wird in 53 Ländern verwendet
Häufig gestellte Fragen
Unsere Daten stammen aus Stellenanzeigen, die von Millionen von Unternehmen gesammelt wurden. Wir überwachen diese Anzeigen auf Firmenwebseiten, Jobbörsen und anderen Rekrutierungsplattformen. Die Analyse von Stellenanzeigen bietet eine zuverlässige Methode, um die von Unternehmen verwendeten Technologien zu verstehen, einschließlich der Nutzung interner Tools.
Wir aktualisieren unsere Daten täglich, um sicherzustellen, dass Sie auf die aktuellsten verfügbaren Informationen zugreifen. Dieser häufige Aktualisierungsprozess garantiert, dass unsere Einsichten und Erkenntnisse die neuesten Entwicklungen und Trends der Branche widerspiegeln.
XGBoost, short for Extreme Gradient Boosting, is a powerful machine learning algorithm known for its efficiency and speed in dealing with structured data. Developed by Tianqi Chen in 2014, XGBoost has gained immense popularity for its ability to provide high performance on a variety of tasks, including classification, regression, and ranking problems. It is based on the concept of gradient boosting, where new models are trained to correct errors made by existing models.
In the category of Python Build Tools, XGBoost stands out as a versatile tool that is commonly used for building machine learning models. It is particularly popular in data science and analytics for its accuracy and speed, making it a preferred choice for professionals looking to optimize their predictive modeling tasks. With its efficient implementation and support for parallel processing, XGBoost has become a go-to solution for a wide range of applications in the field.
Having rapidly gained traction since its inception, XGBoost has established a significant presence in the machine learning community. It currently holds a substantial market share within the domain of gradient boosting algorithms, with many professionals and organizations leveraging its capabilities to enhance their data analysis workflows. As the demand for advanced analytics and predictive modeling continues to grow, XGBoost is expected to maintain its market position and potentially expand further due to its proven performance and widespread adoption.
XGBoost is a powerful machine learning algorithm that has become a popular choice for companies looking to enhance their predictive modeling capabilities. With its exceptional performance and versatility, XGBoost offers a wide range of benefits that set it apart from other machine learning algorithms in the Python Build Tools category.
XGBoost is known for its speed and efficiency in handling large datasets. By utilizing a unique gradient boosting framework, XGBoost can quickly optimize model performance and deliver highly accurate predictions. Compared to traditional machine learning algorithms, XGBoost excels in both speed and accuracy, making it an ideal choice for companies seeking to streamline their predictive modeling processes.
XGBoost offers robust regularization techniques that help prevent overfitting, a common challenge in machine learning. By incorporating L1 and L2 regularization, XGBoost can effectively control model complexity and improve generalization capabilities. This ensures that the model performs well on unseen data, giving companies greater confidence in the reliability of their predictions.
One of the key advantages of XGBoost is its flexibility in customization. Companies can fine-tune various hyperparameters to optimize model performance for specific use cases. This level of customization enables organizations to adapt the algorithm to meet their unique requirements effectively, enhancing the overall predictive accuracy and efficiency of their machine learning models.
XGBoost provides detailed insights into feature importance, allowing companies to understand better the factors driving their predictive models. By analyzing feature importance scores generated by XGBoost, businesses can make more informed decisions regarding feature selection and data preprocessing strategies. This level of transparency and interpretability sets XGBoost apart from other machine learning algorithms, making it a valuable tool for companies striving for data-driven decision-making.
In summary, XGBoost's superior performance, robust regularization techniques, flexible customization options, and enhanced feature importance analysis make it a top choice for companies looking to elevate their predictive modeling capabilities in the Python Build Tools category.
Some notable companies that utilize XGBoost in their tech stack include Airbnb, Uber, and Quora. These industry leaders leverage XGBoost, a popular machine learning algorithm known for its efficiency and performance, to enhance various aspects of their business operations.
Airbnb: Airbnb utilizes XGBoost for its recommendation system to personalize property suggestions for users based on their preferences and past interactions. The company started using XGBoost in 2017 and has since seen significant improvements in user engagement and booking rates. By leveraging XGBoost's predictive capabilities, Airbnb has been able to enhance the overall customer experience on its platform.
Uber: Uber employs XGBoost for dynamic pricing algorithms, enabling the company to optimize fares based on real-time demand and supply data. Since integrating XGBoost into its pricing strategy in 2016, Uber has achieved more accurate pricing predictions and increased profitability. XGBoost has allowed Uber to adapt its pricing dynamically, resulting in improved driver earnings and passenger satisfaction.
Quora: Quora leverages XGBoost to enhance its content recommendation engine, offering users personalized question suggestions based on their interests and browsing history. By incorporating XGBoost into its recommendation system in 2018, Quora has seen a significant increase in user engagement and content discovery. The algorithm has helped Quora deliver more relevant content to its users, leading to higher retention rates and increased platform activity.
Sie können eine aktuelle Liste von Unternehmen, die XGBoost verwenden, auf TheirStack.com einsehen. Unsere Plattform bietet eine umfassende Datenbank von Unternehmen, die verschiedene Technologien und interne Tools nutzen.
Bis jetzt haben wir Daten von 1,993 Unternehmen, die XGBoost verwenden.
XGBoost wird von einer Vielzahl von Organisationen in verschiedenen Branchen, einschließlich "Financial Services", "Financial Services", "Professional Services", "Technology, Information And Internet", "Software Development", "It Services And It Consulting", "Software Development", "Insurance", "Retail", "Financial Services", verwendet. Für eine umfassende Liste aller Branchen, die XGBoost nutzen, besuchen Sie bitte TheirStack.com.
Einige der Unternehmen, die XGBoost verwenden, umfassen JPMorgan Chase Bank, N.A., Affirm, EY, Cash App, Instacart, Nexient, Yelp, Clearcover, Tesco, BMO Financial Group und viele mehr. Sie können eine vollständige Liste von 1,993 Unternehmen, die XGBoost nutzen, auf TheirStack.com finden.
Basierend auf unseren Daten ist XGBoost am beliebtesten in Vereinigte Staaten (829 companies), Vereinigtes Königreich (148 companies), Frankreich (87 companies), Deutschland (63 companies), Indien (62 companies), Kanada (58 companies), Spanien (41 companies), Brasilien (32 companies), Australien (26 companies), Niederlande (24 companies). Es wird jedoch von Unternehmen auf der ganzen Welt verwendet.
Sie können Unternehmen, die XGBoost verwenden, finden, indem Sie auf TheirStack.com danach suchen. Wir verfolgen Stellenanzeigen von Millionen von Unternehmen und nutzen sie, um herauszufinden, welche Technologien und internen Tools sie verwenden.
Es gibt 0 Alternativen zu XGBoost