The phrase "dealing with datasets" is grammatically correct and can be used in written English.
It is often used in fields such as data analysis, research, and computer science. Example: In the presentation, we will be discussing various methods of dealing with datasets, including data cleaning, data visualization, and statistical analysis.
AHNN focuses on dealing with datasets in high-dimension.
In this way, we tested the robustness of our approach when dealing with datasets of different sizes and random collections of subsets of the data.
To estimate the electricity demand function, this study adopts a partial adjustment approach (e.g., Cuddington and Dagher 2015), an effective method when dealing with datasets with micro panels such as a large J (number of groups) and small T (time series).
These two recoding strategies may prove useful to minimize some biases when dealing with datasets with known heterogeneous composition across sequences.
YFH and CCH developed and implemented the overall framework and drafted the manuscript; YCL provided valuable suggestion on dealing with dataset and protein-DNA complexes.
It is desirable to design efficient algorithms in the classification stage to deal with datasets obtained from real-time pattern recognition systems.
In this paper, we extend these algorithms by showing how to split problems by both examples and features simultaneously, which is necessary to deal with datasets that are very large in both dimensions.
In particular, we first illustrate the design of efficient algorithms that exploit the structure of the OLS problems and eliminate redundant computations; then, we show how to effectively deal with datasets that do not fit in main memory; finally, we discuss how to cast the computation in terms of efficient kernels and how to achieve scalability.
Some of the techniques have the ability to handle datasets of high dimensionality and fast in execution, while others are limited in their ability to handle uncertainties, difficult to learn, and could not deal with datasets of high or low dimensionality.
With the advent of advanced real-time data acquisition tools such as LWD, MWD, and SWD coupled with the recent need to integrate all manners of data from well logs through seismic to nuclear magnetic resonance (NMR) for improved reservoir modeling and predictions, petroleum engineers have had to deal with datasets of increasingly high dimensionality.
Therefore, econometricians have to deal with datasets consisting of hundreds of series, thus making the use of large dimensional dynamic factor models (DFMs) more attractive than the usual vector autoregressive (VAR) models, which usually limit the number of variables; see Boivin and Ng (2006).
