关于“Performing Advanced Feature Engineering in Keras”的评价

Pvrmallikharjuna rao P. · 评论almost 5 years之前

Juliana B. · 评论almost 5 years之前

Francesco N. · 评论almost 5 years之前

Nikita K. · 评论almost 5 years之前

What happened to the processed timestamp feature?

Marcus W. · 评论almost 5 years之前

Manfred S. · 评论almost 5 years之前

Shubham K. · 评论almost 5 years之前

Murilo A. · 评论almost 5 years之前

Chan S. · 评论almost 5 years之前

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John K. · 评论almost 5 years之前

Eric T. · 评论almost 5 years之前

Good!

Alex C. · 评论almost 5 years之前

1. The provided csv does not match the expected column order in the notebook. It ends up reading passenger_count as if it was pickup_datetime, giving dates such as "1" and "5" -- which are not parseable as dates 2. The lab asks the student to parse pickup datetime to add it as a feature column, but ultimately that is not possible due to the input data -- the parsed picks the wrong column as pickup_datetime, and the csv does not have a pickup_datetime column which could be parsed as a result, the student will waste time trying to parse invalid columns and probably won't be able to get to the point where they would get to use such columns for training. The lab solution currently present in git (2021-04-06) gets around this problem by not using the pickup_datetime column at all -- it just defines functions to parse datetime, then drops pickup_datetime from the input. The proposed model ends up using a temporal feature completely by accident: it uses passenger_count as a feature -- but on the csv, the column taken as passenger_count is actually hourofday, already present in the dataset. As a result, the model inputs are incorrectly named, and predicting with passenger_count=5 is actually taking into account the predicted price for a ride at 5am

Rodrigo K. · 评论almost 5 years之前

Samuel Angel R. · 评论almost 5 years之前

Manu M. · 评论almost 5 years之前

Lu Y. · 评论almost 5 years之前

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Merin S. · 评论almost 5 years之前