Jupyter Notebook

Additional packages for data visualization support must be installed to plot charts in Jupyter Notebook.

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:
Example
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.

An example of a plotted chart