Examples of using Wrong data in English and their translations into Chinese
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Political
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Ecclesiastic
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Programming
Wrong data, wrong policies.
Wrong data often leads to wrong decision making.
It operates on the wrong data structure.
The problem is not the lack of data, but the wrong data.
Smart cards can also filter out the wrong data to reduce the burden of host CPU.
You have been operating with the wrong data.
Smart cards can also filter out the wrong data to reduce the burden of host CPU.
Horowitz: Most of those stories are based on wrong data.
Smart cards can also filter out the wrong data to reduce the burden of host CPU.
Furthermore, the baseline had been established using the wrong data.
Smart cards can also filter out the wrong data to reduce the burden of host CPU.
In some cases,marketers are collecting too much of the wrong data;
The smart card also filters out the wrong data to reduce the burden on the host CPU.
In this case, Kavin hired the wrong people,who fed him the wrong data.”.
The smart card also filters out the wrong data to reduce the burden on the host CPU.
It helps you avoid creating inaccurate models orbuilding accurate models on the wrong data.
If the arbitrator confirms wrong data, you can sue him and get your money back.
Wrong data- or erroneous recommendations from a“smart” machine- lead to wrong decisions and waste of resources.
The underlying principle for the Expert Meeting was that wrong data lead to wrong policies.
If the wrong data been registered or you have any other objections, you may contact the same place.
It helps you avoid accidentally creating inaccurate models,or building accurate models that are built on the wrong data.
For instance, if you're receiving the wrong data for a field, it's a sign that form labels are unclear;
And this is not merely just a question of a materialistic interpretation of the data, but actually presenting wrong data and ignoring others.
Session VI Wrong data, wrong polices(PowerPoint presentation by Hafiz Mirza, UNCTAD/DITE).
Even with only a quarter of the dataset mislabeled,it's clear how much impact wrong data can have on how we create our model.
Acting on the wrong data- or wrong recommendations from a“smart” machine- will lead to bad decisions and wasted resources.
It shows how critical training data is:give an AI system the wrong data to learn from, and it will learn the wrong thing.
We examine typical mistakes in Data Science process,including wrong data visualization, incorrect processing of missing values,wrong transformation of categorical variables, and more.
Article 14 is only concerned with" input" errors, that is,errors relating to inputting wrong data in communications exchanged with an automated message system.