Examples of using Dataset in English and their translations into Marathi
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We have that dataset.
This dataset has 3 classes and 4 features.
No related datasets found.
Large datasets are a means to an end; they are not an end in themselves.
With this larger dataset, repeat part(d).
Finally, replicate the same plot with the 2nd version, English fiction dataset.
Third, large datasets enable researchers to detect small differences.
I just used the whole available dataset to normalize features.
Ultimately, the dataset was removed from the Internet, and now it cannot be used by other researchers.
First, it means that attempting to“anonymize” the dataset based on random perturbation will likely fail.
Large datasets can also create computational problems that are generally beyond the capabilities of a single computer.
Replicate the same plot using 1 1st version of the corpus,English dataset(same as Fig. 3A, Michel et al.).
With this merged dataset, Costa and Kahn found that the Home Energy Reports produced broadly similar effects for participants with different ideologies;
While that might be true in general,for some of the 500,000 people in the dataset, movie ratings might be quite sensitive.
Big datasets also seem to lead some researchers to ignore how their data was created, which can lead them to get a precise estimate of an unimportant quantity.
Within a few hours, they had created a new crowd-coded dataset that closely matched their original crowd-coded data set.
Having a big dataset enables some specific types of research- measuring heterogeneity, studying rare events, detecting small differences, and making causal estimates from observational data.
The data that they used hasnow been released as the Google NGrams dataset, and so we can use the data to replicate and extend some of their work.
The two ingredients are 1 a digital trace dataset that is wide but thin(that is, it has many people but not the information that you need about each persons) and 2 a survey that is narrow but thick(that is, it has only a few people, but it has the information that you need about those people).
The best way to think about these second-generation systems is that rather than having humans solve a problem,they have humans build a dataset that can be used to train a computer to solve the problem.
The result of this collaborative effort is a massive dataset summarizing the information embedded in these manifestos, and this dataset has been used in more than 200 scientific papers.
I call this a computer-assisted human computation project because, rather than having humans solve a problem,it has humans build a dataset that can be used to train a computer to solve the problem.
In October of 2006, Netflix released a dataset containing 100 million movie ratings from about about 500,000 customers(we will consider the privacy implications of this data release in Chapter 6).
I call this kind of project a second-generation human computational project because, rather than having humans solve a problem,they have humans build a dataset that can be used to train a computer to solve the problem.
The result of this collaborative effort is a massive dataset summarizing the information embedded in these manifestos, and this dataset has been used in more than 200 scientific papers.
In a draft paper accompanying the released data, the authors stated that“Some may object to the ethics of gathering and releasing this data. However, all the data found in the dataset are or were already publicly available, so releasing this dataset merely presents it in a more useful form.”.
In October of 2006, Netflix released a dataset containing 100 million movie ratings from about about 500,000 customers(we will consider the privacy implications of this data release in chapter 6). The Netflix data can be conceptualized as a huge matrix that is approximately 500,000 customers by 20,000 movies.
In response to the data release,one of the authors was asked on Twitter:“This dataset is highly re-identifiable. Even includes usernames? Was any work at all done to anonymize it?” His response was“No. Data is already public.”(Zimmer 2016; Resnick 2016).
It[is] difficult to avoid the conclusion thatwomen were omitted because this‘tailor made' dataset was confined by a paradigmatic logic which excluded female experience. Driven by a theoretical vision of class consciousness and action as male preoccupations…, Goldthorpe and his colleagues constructed a set of empirical proofs which fed and nurtured their own theoretical assumptions instead of exposing them to a valid test of adequacy.”.
It[is] difficult to avoid the conclusion thatwomen were omitted because this‘tailor made' dataset was confined by a paradigmatic logic which excluded female experience. Driven by a theoretical vision of class consciousness and action as male preoccupations…, Goldthorpe and his colleagues constructed a set of empirical proofs which fed and nurtured their own theoretical assumptions instead of exposing them to a valid test of adequacy.”.