| 释义 |
Definition of datapoint in US English: datapointnounˈdatəˌpointˈdātə- An identifiable element in a data set. software that can quickly process tens of thousands of datapoints Example sentencesExamples - The slopes were evaluated from the tangents of the linear fit for all the datapoints between two points i and i + NP - 1 (NP = number of datapoints over the distance D) for all the points i in the height profile.
- Simply removing either the last two groups or the four datapoints with the highest acceptor levels results in a reduced value of p = 0.2454, indicating a lack of evidence of dependence.
- Support vector machines can be phrased as statistical query algorithms, but the number of queries scales with the number of datapoints.
- Dividing the data into six groups of ~ 25 datapoints, with increasing uD + A, shows a greater level of heterogeneity in the local density s-values while maintaining a strong negative dependence of s on uD + A.
- The whole set of 40 datapoints allows us to determine a good average of k 0 and [beta]. Only for the tryptophans of NSCP is an additional lifetime found, which might be an indication for additional sources of heterogeneity.
Definition of datapoint in US English: datapointnounˈdātə- An identifiable element in a data set. software that can quickly process tens of thousands of datapoints Example sentencesExamples - The whole set of 40 datapoints allows us to determine a good average of k 0 and [beta]. Only for the tryptophans of NSCP is an additional lifetime found, which might be an indication for additional sources of heterogeneity.
- Support vector machines can be phrased as statistical query algorithms, but the number of queries scales with the number of datapoints.
- The slopes were evaluated from the tangents of the linear fit for all the datapoints between two points i and i + NP - 1 (NP = number of datapoints over the distance D) for all the points i in the height profile.
- Simply removing either the last two groups or the four datapoints with the highest acceptor levels results in a reduced value of p = 0.2454, indicating a lack of evidence of dependence.
- Dividing the data into six groups of ~ 25 datapoints, with increasing uD + A, shows a greater level of heterogeneity in the local density s-values while maintaining a strong negative dependence of s on uD + A.
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