Additional packages for data visualization support must be installed to plot charts in Jupyter Notebook.
Choose the suitable code to plot the information regarding previously launched or ongoing trainings:
Add a training parameter
It is possible to plot a chart while training by setting the plot parameter to “True”. This approach is applicable for the following methods:
from catboost import CatBoostClassifier, Pool train_data = [[1,3], [0,4], [1,7], [0,3]] train_labels = [1,0,1,1] catboost_pool = Pool(train_data, train_labels) model = CatBoostClassifier(learning_rate=0.03) model.fit(train_data, train_labels, verbose=False, plot=True)
Read data from the specified directory only using MetricVisualizer
import catboost w = catboost.MetricVisualizer('/path/to/trains/1') w.start()
Gather and read data from all subdirectories of the specified directory using MetricVisualizer
import catboost w = catboost.MetricVisualizer('/path/to/trains', subdirs=True) w.start()
Any data located directly in the /path/to/trains folder is ignored. The information is read from its' subdirectories only.