EACH DATA POINT in Chinese translation

• 每个数据点

Examples of using Each data point in English and their translations into Chinese

Scaling a variable is multiplying each data point by a constant in order to alter the range of the data..
缩放变量就是对每个数据点乘以一个常数来改变数据的范围。

Algorithms such as K-Means clustering work by randomly assigning initial“proposed” centroids, then reassigning each data point to its closest centroid.
诸如K-Means聚类的算法通过随机分配初始“建议”质心，然后将每个数据点重新分配到其最接近的质心来工作。

Normally, it would take a human roughly 800 hours to annotate each data point, Tandon said, but Drive.
坦顿表示，在通常情况下，人类需要大约800个小时来为每个数据点做注解，但Drive.

You can start with a belief, but each data point will either strengthen or weaken that belief and you update your hypothesis all the time.
你可以从一个可信度开始，但每个数据点都会强化或削弱这种可信度，并且你会一直更新你的假设。

For each data point.
对于每个数据点，要么，要么。

Each data point is a provider.
每个数据点都是一个客户。

With each data point, I will keep measuring.
每个数据点，我会继续测量的。

Each data point includes the sum for the week.
每个数据点都会包含此周的总数。

Meticulously hand-plotted each data point on yellowed graph paper.
仔细用手在发黄的图表纸上画出每个数据点。

Randomly assign each data point to any of the 3 clusters.
B）将每个数据点随机分配到3个群集中的任何一个。

Each data point is the average of 100 realizations.
每个评估分数是100episode返回的平均值。

Numbers to define the drawing order of each data point(location).
用于定义每个数据点（位置）的绘制顺序的数字。

Each data point is the mean of 10 replicates of 25 seeds.
每个数据点都代表25粒种子10个重复的平均值。

A key element is a description of how each data point was created.
一个关键要素是描述每个数据点是如何生成的.

At the start of the clustering exercise, each data point is a cluster.
在这个算法的起始阶段，每个数据点都是一个簇。

Markers- Whether a column or line chart shows the value of each data point.
Markers-柱形图或折线图是否显示每个数据点的值。

Complete dataset labeled to ensure each data point is assigned to one of the clusters.
数据集的完全标记，以确保每个数据点都可以分配给其中一个聚类。

In this technique, multiple models are used to make predictions for each data point.
这种技术中使用多个模型来预测每个数据点。

To calculate that, we find how far each data point is from the average.
平均差告诉我们，每个数据值平均离中心有多远。

These constraints state that each data point must lie on the correct side of the margin.
这些限制表明每个数据点都必须位于间隔的正确一侧。