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Annotation 7100434877708

#recurrent-neural-networks #rnn
enables us to simulate future transactions at a very fine granular level and attribute them to the right customer (or any subgroup of the customer-base) and calendar time without prior domain knowledge. We explore the capabilities of this novel forecasting approach to customer base analysis in detail, and benchmark the proposed model against established probabilistic models with latent attrition, as well as a non-parametric approach based on Gaussian process priors, in very diverse non-contractual retail and charity scenarios. Our model raises the bar in predictive accuracy on both the individual customer and the cohort level, automatically capturing seasonal and other temporal patterns
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Flashcard 7628313726220

Tags
#DAG #causal #edx #has-images #inference
[unknown IMAGE 7096178707724]
Question
As you may have already noticed, the case-control design selects individuals based on their outcome. Women who did develop cancer are [...] to be included in the study than women who did not develop cancer. Therefore, our causal graph will include a note for selection-- C-- an arrow from the outcome Y to C, and a box around C to indicate that the analysis is conditional on having been selected into the study, which means that we are only one arrow away from selection bias.
Answer
much more likely

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As you may have already noticed, the case-control design selects individuals based on their outcome. Women who did develop cancer are much more likely to be included in the study than women who did not develop cancer. Therefore, our causal graph will include a note for selection-- C-- an arrow from the outcome Y to C, and a box around

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Flashcard 7628315823372

Tags
#causality #statistics
Question
Given that we have tools to measure association, how can we isolate causation? In other words, how can we ensure that the association we measure is causation, say, for measuring the causal effect of 𝑋 on 𝑌 ? Well, we can do that by ensuring that there is [...] association flowing between 𝑋 and 𝑌
Answer
no non-causal

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solate causation? In other words, how can we ensure that the association we measure is causation, say, for measuring the causal effect of 𝑋 on 𝑌 ? Well, we can do that by ensuring that there is <span>no non-causal association flowing between 𝑋 and 𝑌 <span>

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Annotation 7628317658380

#recurrent-neural-networks #rnn

non-contractual settings

The specific challenge in such settings is to accurately and timely inform managers on the subtle distinction between a pending defection event (i.e., a customer stops doing business with the focal firm) and an extended period of inactivity of their customers, because possible marketing implications are completely different in each of these situations.
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n-contractual business settings is by definition unobserved by the firm and thus needs to be indirectly inferred from past transaction behavior (Reinartz & Kumar, 2000; Gupta et al., 2006). <span>The specific challenge in such settings is to accurately and timely inform managers on the subtle distinction between a pending defection event (i.e., a customer stops doing business with the focal firm) and an extended period of inactivity of their customers, because possible marketing implications are completely different in each of these situations. <span>

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Flashcard 7628320804108

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
Since customer transactions occur sequentially, they can be modeled as a sequence prediction task using an RNN as well, where all firm [...] and customer responses are represented by elements in a vector.
Answer
actions

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Since customer transactions occur sequentially, they can be modeled as a sequence prediction task using an RNN as well, where all firm actions and customer responses are represented by elements in a vector.

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Annotation 7628322114828

#feature-engineering #lstm #recurrent-neural-networks #rnn
The HMM has N discrete hidden states (where N is typically small) and, therefore, has only log 2 (N) bits of information available to capture the sequence history (Brown & Hinton, 2001)
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The HMM has N discrete hidden states (where N is typically small) and, therefore, has only log 2 (N) bits of information available to capture the sequence history (Brown & Hinton, 2001). On the other hand, the RNN has distributed hidden states, which means that each input generally results in changes across all the hidden units of the RNN (Ming et al., 2017). RNNs comb

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Flashcard 7628323949836

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
HMM == Hidden [...] Model
Answer
Markov

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HMM == Hidden Markov Model

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Flashcard 7628325260556

Tags
#RNN #ariadne #behaviour #consumer #deep-learning #priority #recurrent-neural-networks #retail #simulation #synthetic-data
Question
Recognizing relevant patterns in long input streams (> 100 actions) can turn out to be difficult for the human mind. To achieve better explainability, in many e-commerce applications consumer behavior can be viewed on the level of [...]
Answer
sessions

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put streams (> 100 actions) can turn out to be difficult for the human mind. To achieve better explainability, in many e-commerce applications consumer behavior can be viewed on the level of <span>sessions <span>

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