Synthesizing Data Analytics towards Intelligent Enterprises
Bibliographic record
Abstract
In today's world the amount of data available to organizations every day continues to proliferate at a staggering volume. Using them in an efficient way enterprises will be able to forecast revenues more accurately, improve overall business and make better decisions about new product investment. Data analytics plays a key role to use these datas effectively and can help enterprises to be more resilient, profitable and sustainable. The data driven from enterprises naturally fall into four different kinds of data analytics namely Descriptive, Diagnostic, Predictive & Prescriptive depending on the question it helps to answer. These can equip the decision makers to describe past results, diagnose past results occurrence, predict future happenings and recommend the necessary actions for the organization's next steps. Armed with deeper insights and recommendations the enterprises can gain a better understanding of their performance as a whole and can make better decisions as a result are termed as Intelligent enterprises. In this work, we will apply a mix of machine learning algorithms like Stacked LSTM model and Tf-idf vectorizer which have been utilized for different types of prediction. The core idea is to showcase of these types of algorithms can effectively predict various kinds of outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".