What are the most interesting parts of building a ML classifier? 1. feature engineering 2. label engineering 3. experimenting with different models 4. hyperparameter tuning 5. summarizing/explaining model performance 6. automating training and deployment 7. <something I missed>
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Replying to @seanjtaylor @MrMeritology
Picking a Good Problem (™). I don't mean that in a snarky way. I mean it sincerely and in earnest. Everything you listed can interesting and fun. (I personally like label engineering the most.) But, the quality of your problem selection sets a hard ceiling.
12:33 PM - 24 Nov 2018
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