Examples of using Data preparation in English and their translations into Chinese
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Data preparation.
Part three is data preparation.
Data preparation in the CRISP-DM model.
To use it, you don't have to do any data preparation or manage models.
Precise data preparation is the key to obtaining high-quality, efficient data. .
They are involved in four basic areas of forensic analytics:data collection, data preparation, data analysis and reporting.
Very less data preparation is required.
Today, BI vendors arealready introducing AI capabilities in their products for data preparation, data discovery, and data science.
Very less data preparation is required.
TensorFlow supports the HDFS, integrates big data and deep learning,and completes the chain from data preparation to model training.
In the data preparation part, the participants focused on the change detection analysis.
We are investing in this space in a number of areas: Recommendations,smart data preparation, automatic model generation, and automated discovery.
This software reduces data preparation time by 90%, cuts support removal time by 50% and allows a powder recuperation of nearly 100%.
On the other hand, real-time data update also greatly improves the work efficiency of analysts andsaves a lot of repetitive data preparation work.
Those efforts will primarily concern the data preparation, implementation and initial operations of Releases 3 and 4.
Data preparation to cleanse, transform and reorganize data into a format suitable for predictive analytics or machine learning algorithms.
Every time new data is received,analysts need to repeat manual data preparation tasks to adjust the structure and clean the data for analysis.
Data preparation and preprocessing play important roles in the deep learning and training process, and affect the speed and quality of model training.
As many others note, this effort in data collection and data preparation can in fact be the most substantial component of a data mining project.
Data preparation is the procedure collecting, cleaning, and consolidating data into one file or data table, primarily for use in the analysis.
For example, Facebook, Google and Uber have built FBLearner Flow, TFX,and Michelangelo to manage data preparation, model training and deployment.
Financial Management's data preparation server can ease integrating and validating financial data from any source system.
Design and build activities will be supported by associated detailed business process reengineering,legacy data preparation, data cleansing and change management.
Augmented Data Preparation, common business users will be able to test the data against particular hypotheses without the help of IT staff.
Although ARIMA is a very powerfulmodel for forecasting time series data, the data preparation and parameter tuning processes end up being really time consuming.
Other features include sentiment analysis for extracting data from social media and other texts, automatic generation of charts, mapping,and self-service data preparation.
Many have invested in their own self-service data preparation, visualization and analytics tools, while others have even employed their own data scientists.
These tools fit into a variety of categories, including data integration,data virtualization, data preparation, ETL, data quality and data governance.
These tools, which also encompass things such as self-service data preparation software, provide connectors to mainstream relational database management systems and newer NoSQL databases.
Based on the business understanding, data understanding, data preparation, modeling, and evaluation sub-steps, CRISP proceeds directly to the deployment of results in business processes.