ML: Li-ion 🔋 Crystal Structure

The video tutorial explores a case study on lithium ion batteries, focusing on predicting crystal structures using feature engineering. It covers various encoding methods, including one-hot encoding, ordinal encoding, and feature hashing, to transform categorical data into numerical data. The tutorial also demonstrates the use of the chem parse package to decompose chemical formulas into elemental counts. The effectiveness of different encoding methods is tested using a decision tree classifier, highlighting the importance of preserving categorical data for accurate predictions.

What is the role of the chem parse package in the feature engineering process for lithium ion batteries?

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What are the potential outcomes of using ordinal encoding for the space group feature?

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Explain the difference between a multi-class problem and a multi-label problem in the context of predicting crystal structures.

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Discuss the importance of balancing the dataset when predicting crystal structures.

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