Musical preferences prediction by classification algorithm
Bibliographic record
Abstract
In this paper, we use several supervised classification algorithms to predict musical preference of a person. From psychological point of view, although personal emotion is an important feature that has an influence on selecting music, there are some other significant factors such as age, sex, education and district that might have an impact on our musical choices. In this paper, we first collected our data based on an observation method called stratified sampling. In this model, we collected 2000 cases that were grouped into strata (as district in our data feature), then simple random sampling was employed within each stratum. We partitioned our original dataset into two classes, 60% of which we were used to train our models and 40% of which we were held back as a validation dataset. The dataset contains five features as follows: four features named sex, age, education and district as explanatory variables and one feature named music known as response or target variable. The response variable has two different levels, namely traditional and non-traditional so we were dealing with a binary classification. The dataset that we created is called MPD. Moreover, we calculated some important statistical measures such as accuracy, specificity, precision, sensitivity and F-measure. Finally, we examined four different algorithms using R which were a nice mixture of nonlinear (cart, knn) and complex nonlinear methods (rf) and the result in random forest had the highest accuracy with 86.8%. We also observed that the highest F-measure is gained by cart algorithm with 44.7% score. As we have not considered the person's emotion as an influential factor on musical choices, we could expect the accuracy of learning algorithms would not react at very high performance. Our results proved this claim.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".