Plot to see performance of S-Rerf against other classifiers on time Series data

[sanika1201]

Generating a plot displaying the performance of S-Rerf against other classifiers on EEG data collected for grasp and lift actions. The data is available here: https://www.kaggle.com/c/grasp-and-lift-eeg-detection/data.

Plan to upload a mean test error plot comparing classifiers used previously for this data with S-Rerf.

[sanika1201]

Description Goal: Compare performance of S-Rerf with different classifiers on grasp detection using real EEG data.

This demo is a Jupyter Notebook documentation analyzing the performance of S-Rerf against classifiers like K-Nearest Neighbors, Random Forest and Multi-Layer Perceptron on structured EEG data. To keep the structure of the data, binning (based on the concept of moving average filter) is done before training on the data. The challenge faced is that the data is highly unbalanced so it is balanced before training. The metric used for evaluation are precision curves, balanced accuracy and mean test error.

Output: The precision, balanced accuracy and mean test error plots that compare performance of S-Rerf with different classifiers.

Code and Details of the demo: https://nbviewer.jupyter.org/github/NeuroDataDesign/team-forbidden-forest/blob/master/Sanika/Final_PR_upload.ipynb