Comments by "Fernando Jackson" (@fernandojackson7207) on "freeCodeCamp.org" channel.

  1. Excelent Presentation. Are the models ever made explicit or are they black-boxed, e.g., for regression models we have an explicit rep y=mx+b. Is the same the case for other models?
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  2.  @malek3764  Thank you. Do we at least know the type/category of model? Is this multilinear regression, Logistic Regression, etc?
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  3.  @malek3764  Thanks again. One last one, please: How do we actually use the model for prediction? Say I have a few data entries, as, say a Numpy array for the problem as the one in this video: Each entry would be a string (Age, Gender, etc ). How can I apply the resulting model to test whether they were predicted to survive? Or, for other models, to, say decide if an animal in a pic is a cat or a dog. How do we accept input data and get our output , e.g., (cat, cat, dog,.., etc)?
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  4. Parch =Parents + Children, the number of each.
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  5. I dont mean to be that guy butthis is not standard linear regression. Maybe Tim knows this and doesnt want to go into it too deeply but this is logistic regression.
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  6. I am a bit confused here : If we want 5% , why dont we select 10 that are not healthy and then 190 that are: 10/(190+10) =.05=5% but 10/(10+200) is not 5%. But maybe I am missing something?
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  7. Tim, you have a special talent for explaining. Brilliant.
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