As freshers and early Data Science and AI career aspirants, this series will help you prepare better for your upcoming roles and interviews. It provides a clear and concise explanation of key concepts, related to Accuracy in Machine Learning and Data Science, which are frequently asked about in Data Science interviews.
𝟏. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐨𝐟 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠?
𝟐. 𝐖𝐡𝐲 𝐦𝐢𝐠𝐡𝐭 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐧𝐨𝐭 𝐛𝐞 𝐚 𝐠𝐨𝐨𝐝 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 𝐢𝐧 𝐬𝐨𝐦𝐞 𝐜𝐚𝐬𝐞𝐬?
𝟑. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐚𝐧𝐝 𝐩𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧?
𝟒. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐚𝐧𝐝 𝐅𝟏 𝐬𝐜𝐨𝐫𝐞?
𝟓. 𝐂𝐚𝐧 𝐚 𝐦𝐨𝐝𝐞𝐥 𝐡𝐚𝐯𝐞 𝐡𝐢𝐠𝐡 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐛𝐮𝐭 𝐬𝐭𝐢𝐥𝐥 𝐛𝐞 𝐢𝐧𝐞𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞? 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐰𝐢𝐭𝐡 𝐚𝐧 𝐞𝐱𝐚𝐦𝐩𝐥𝐞.
The document also highlights the limitations of accuracy as a sole metric, especially in imbalanced datasets, and introduces alternative metrics to provide a more comprehensive evaluation of a model’s performance. This knowledge can help candidates demonstrate a deeper understanding of model evaluation and selection, which is crucial for securing a Data Science role.
Hope you find this insightful.
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