3.1.2.8. Analyzing machine learning model training results

This page briefly summarizes the contents of the Jupyter notebook at the file path <root>/examples/modelling/cbed/distortion/estimation/notebooks/analyzing_ml_model_training_results.ipynb, where <root> is the root of the emicroml repository.

In this notebook, we analyze the output that results from performing the “actions” described in the following pages:

1. Generating machine learning datasets for training and validation

2. Combining then splitting machine learning datasets for training and validation

3. Training a machine learning model

while also demonstrating how one can use a selection of the functions and classes in the module emicroml.modelling.cbed.distortion.estimation. In short, in this notebook we analyze machine learning (ML) model training results for the ML task of estimating distortion in convergent beam electron diffraction (CBED).

In order to execute the cells in this notebook, a set of Python libraries need to be installed in the Python environment within which the cells of the notebook are to be executed. See this page for instructions on how to do so.

It is recommended that you consult the documentation of the emicroml library as you explore the notebook. Moreover, users should execute the cells in the order that they appear, i.e. from top to bottom, as some cells reference variables that are set in other cells above them.