A Hybrid Machine Learning Approach to IT Salary Prediction: Insights from Academic, Demographic, and Socio-Economic Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IT professional’s first job after graduation is a significant milestone, marking the first step to financial independence. To explore the factors influencing compensation for IT graduates, a binary classification model using an ensemble machine learning approach that integrates Random Forest (RF), Neural Networks (NN), and LightGBM was developed. A multilevel strategy was adopted, beginning with training the data using RF and subsequently feeding the output leaf indices and the feature set into NN, culminating with LightGBM functioning as a meta-classifier. A cross-validation approach was employed to assess the model’s accuracy rigorously. The model achieved an 88% accuracy rate and an F1 score exceeding 80% across all categories. Utilizing SHAP analysis, key features per model were extracted and analyzed. Notable features highlighted by the two models are the mother’s educational level, IT experience, degree concentration, study frequency, accommodation, siblings and grades. Features such as holding a degree in cybersecurity and residing on dorms emerged as significant predictors of higher starting salaries. The model offers valuable insights for students, enabling them to enhance their qualifications and improve their compensation prospects after graduation
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it