GUI interface for machine learning toolbox¶
The toolbox is a convenient tools for exploring data and algorithms.
from mygeopackage import toolbar
toolbar.ml_tool()
To use the GUI interface, import the toolbar module from mygeopackage, then initialized the ml_tool() function.
Read Data¶
from IPython.display import Video
Video(r'assets\Read_Data.mp4',height=400,width=600,embed=True)
Click Select, and then choose the file you'd like to read. Click Read Data to invoke the ML toolbox.
Video(r'assets\Normalized.mp4',height=400,width=600,embed=True)
To use the Data Preprocessing module, click the Data Preprocessing tab, and then choose the field you'd like to normalized and click Apply. The Data Preprocessing module will replace the oringal column with the normalized results.
Video(r'assets\K_Means.mp4',height=400,width=600,embed=True)
Move on to the Unsupervised tab. There are two tools available under the tab, K-Means and DBSCAN. To perform K-Means, set the Desired Clusters, then choose multiple fields that you'd like to perform clustering in Fields. Finally, select the identifier of the dataset and click Apply. The results will show on the map below.
Video(r'assets\DBSCAN.mp4',height=400,width=600,embed=True)
To perform DBSCAN, set the EPS and Min_Samples. EPS must be a float value. Select multiple fields from Fields, and choose an identifier for the dataset. Click Apply, and then the results in the map below.
Video(r'assets\Regression.mp4',height=400,width=600,embed=True)
To perform regression analysis, move to the Regression type. Select a field from Dependent Variable, then select multiple Independent Variables from the list. Finally, choose the identifier for the dataset. The residuals will be shown on the map, and you can check the R square score under. This tool is useful to explore the spatial distribution of residuals.