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on 24-Aug-2023 (Thu)

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Annotation 7103933975820

#feature-engineering #lstm #recurrent-neural-networks #rnn
For this exercise, the authors developed two separate LSTM models. The first one predicted the likelihood that each donor was going to respond favorably to the solicitation (0/1), and we calibrated it on the entire calibration data (N = 61,928). The second LSTM model predicted the donation amount t in case of donation, and we calibrated it on the individuals who donated in the calibration data (N = 6,456)
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Flashcard 7584631360780

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
For this exercise, the authors developed two separate LSTM models. The first one predicted the likelihood that each donor was going to [...] to the solicitation (0/1), and we calibrated it on the entire calibration data (N = 61,928). The second LSTM model predicted the donation amount t in case of donation, and we calibrated it on the individuals who donated in the calibration data (N = 6,456)
Answer
respond favorably

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For this exercise, the authors developed two separate LSTM models. The first one predicted the likelihood that each donor was going to respond favorably to the solicitation (0/1), and we calibrated it on the entire calibration data (N = 61,928). The second LSTM model predicted the donation amount t in case of donation, and we calibrated

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Annotation 7584633457932

#feature-engineering #lstm #recurrent-neural-networks #rnn
While training a model, the analyst aims at setting the parameters and hyperparameters such that the model reaches optimal capacity (Goodfellow et al., 2016) and therefore maximizes the chances that the model will generalize well to unseen data.
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While training a model, the analyst aims at setting the parameters and hyperparameters such that the model reaches optimal capacity (Goodfellow et al., 2016) and therefore maximizes the chances that the model will generalize well to unseen data. Models with low capacity would underfit the training set and hence have a high bias. However , models with high capacity may overfit the training set and exhibit high variance. Represen

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