In a recent analysis, 75% of data scientists and statisticians agreed that TimeSeriesSplit is an appropriate method for training and validation when predicting purchase propensity. The dataset in question includes client ID, quote ID, date on a daily basis, a target variable indicating whether the client purchased the service, and several other features. The goal is to predict the likelihood of a client completing a purchase after receiving a quote. Despite the target variable not being a continuous time-dependent variable, the majority of experts consider this a time series problem. This consensus highlights the importance of considering temporal aspects in predictive modeling, even when the outcome is binary.
𝗰𝘆𝗰𝗹𝗼𝗽
@nobrainflip
·
Feb 25 Now is the most divided timeline I’ve ever seenBulls believe this is last dip before altsznBears think bull run is overI spent 10+ hours analyzing all the data: here are all the possible scenarios and when market will turn around
HodlFocus
@HodlFocus
·
20h $PROMPT by
@AIWayfinder
is looking like a good buy here. It’s showing that its respecting key Fibonacci lines. If we reverse it we can see that after it goes into price discovery, the next key level is .80 and after that 1.20.
𝐋𝐞𝐦𝐚𝐲𝐢𝐚𝐧
@lemayian001
·
Mar 3 BIAS????????Pick any timeframe ,mark HTf key level (FVG,old high or low etc),then ask if price bounces off this key level where is it likely to gravitate to,that’s your Bias in that timeframe.Zoom in to LTF ,SMT then CISD Find an entry that aligns to that Direction.Repeat
Falcon36
@ByondReaction
·
Apr 15 I suspect it’s a situation where many people distribute based on lower timeframe PA and some accumulate because of the higher timeframe picture.
Sham
@SpeculatorFL
·
Mar 2 When the time is right – Price will definitely reverse.
When the time is not right – Price will fuck your account.It’ll always be supported by a higher timeframe key level + SMT divergence.
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