The idea behind the Label Density strategy is that when labeling a What is the dataset, you want to label where the feature space has a dense cluster of data points.
Feature Space What is the
The feature space represents all the possible combinations recent mobile phone number data of column values (features) you have in the dataset. For example, if you had a dataset with only people’s weight and height, you would have a 2-dimensional Cartesian plane. Most of your data points here will probably be around 170 cm and 70 kg. So, around these values, there will be a high density in the 2-dimensional distribution. To visualize this example, we can use a 2D density plot.
Figure 1: A 2D density plot clearly visualizes the areas with more behind-the-scenes insights dense clusters of data points — here in dark blue.
This type of visualization
Only works when you have a feature space defined only by two as uae phone number Zoom, columns. In this case, the two columns are people’s weight and height, and each data point, the markers on the plot, are the different people.
In Figure 1, density is not simply concentrical to the center of the plot. There is more than one dense area in this feature space. For example, in the picture, there is one dense area featuring a high number of people around 62 kg and 163 cm and another area with people who are around 80 kg and 172 cm. How do we make sure we label in both dense areas, and how would this work if we had dozens of columns and not just two?