THE RESULT
What the data shows
The largest computed metric is 0.7829 for Random forest; the smallest is 0.07239 for Prior baseline. Metric: macro_f1 (Macro F1).
Seven one-hot target indicators were removed during preparation. Healthy plates are not represented.
| model | accuracy | balanced accuracy | macro f1 |
|---|---|---|---|
| Prior baseline | 0.3393 | 0.1429 | 0.07239 |
| Logistic regression | 0.6787 | 0.6901 | 0.6842 |
| Random forest | 0.7455 | 0.7663 | 0.7829 |
THE METHOD
From source to answer
Fixed multiclass classification with grouped identical inputs and macro-F1 evaluation.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
EVALUATION
How the result was checked
Fixed 80/20 split of unique input groups (seed 42); exact input duplicates stay together.
Fixed models; default decision threshold; no holdout tuning.
Target and input features
Target: fault
Inputs: pixels_areas, x_perimeter, y_perimeter, minimum_of_luminosity, maximum_of_luminosity, length_of_conveyer, typeofsteel_a300, typeofsteel_a400, steel_plate_thickness, edges_index, empty_index, square_index, outside_x_index, edges_x_index, edges_y_index, outside_global_index, logofareas, log_x_index, log_y_index, orientation_index, luminosity_index, sigmoidofareas
THE NEXT DECISION
What follows from the finding
Validate against new production batches and healthy examples before considering deployment.
Where the conclusion stops
Every source row is a recorded fault. The data cannot estimate a production defect rate or distinguish healthy plates. Batch and machine IDs are absent, so grouped exact-input holdouts do not prove factory transfer.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.