Random Forest Logistics on Support Vector
4
Upon analysis, entities who disappeared and indicated with high stress have a high risk of deteriorating health. Anxiety and mood swings may be a factor, though not as significant. Algorithms such as MLR Matrix, RF Trees, and SVM were used to conduct these analyses, and RF came out as the most accurate for this particular case. Plans to deploy the model en masse is being orchestrated and should be on the field by March 1st. :: Made to illustrate my journeys in machine learning. ::
machine learning, multiple linear regression, logistic regression, mlr matrix, penguin logistics (arknights), random forest, forest, support vector machines, vector, kriss vector, health risk chart, bar chart, R, kaggle
매개변수 표시
- 크기
- 1024 x 1024
- 샘플링 단계
- 25
- 샘플링 방식
- Euler a
- CFG 스케일
- 5.5
- Seed
- 3416347579360538
- 제외 조건
- nsfw, long neck, deformed, mutated, mutation, ugly, disfigured, poorly drawn face, skin blemishes, skin spots, acne, the wrong limb, lowers, bad anatomy, bad hands, text, error, missing fingers, extra digit,Excess fingers, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,Black line,Excess hands, extra hands, jpeg artifacts
고해상도