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Annotation 7662643055884

#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Generally speaking, explaining the predictions of vector-based methods is more difficult than often assumed. This holds even for linear models like logistic regression. Features are often preprocessed, for example to binarize counts (Sec. 2). Furthermore, they are typically strongly correlated, making it troublesome to interpret individual coefficients [6]. Table 3 shows exemplary features weights in a logistic regression model used to predict order probabilities. If hundreds of features are utilized and are correlated and preprocessed, explaining the impact of consumer actions becomes a complex and confusing task
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res for every time-step. Calculations at timesteps t and t − 1 would be highly redundant: features at t represent the complete history until t and not only what happened in between t − 1 and t. <span>Generally speaking, explaining the predictions of vector-based methods is more difficult than often as- sumed. This holds even for linear models like logistic regression. Features are often preprocessed, for example to binarize counts (Sec. 2). Furthermore, they are typically strongly correlated, making it troublesome to interpret individual coefficients [6]. Table 3 shows exemplary features weights in a logistic regression model used to predict order probabilities. If hundreds of features are utilized and are correlated and preprocessed, explaining the impact of consumer actions becomes a complex and confusing task <span>

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