Hyperparameter Optimization for CNN, K-NN, and Decision Tree in Handwritten Digit Classification
Bibliographic record
Abstract
Handwritten recognition of characters appears to be the most fascinating field of image processing research among the many studies that have been completed.Handwritten character recognition methods use scanned photos, documents, and real-time devices such as tablets, tabloids etc. as input, which is converted into digital text.For machine learning algorithms, hyper-parameters are important since they guide the training process and have a significant impact on model performance.This study thoroughly examines and emphasizes how careful parameter adjustment is necessary to optimize model performance and generalization.Our findings provide valuable insights to obtain high classification accuracy for handwritten digits using machine-learning techniques which are decision tree (DT), convolution neural network (CNN), and k-nearest neighbors (KNN) that are optimized through hyper-parameter tuning techniques: grid search and random search to modifying a machine learning model's to determine the best values of the parameters to enhance the model's.The optimization machine learning models were applied and compared on the MNIST digit database.The suggested techniques were able to identify optimal hyper-parameters for a variety of ML models.Our major goal is to match the accuracy of the classifier models along with their implementation time to obtain the best possible model for digit recognition.the outcome of our work indicate an accuracy rate of 97.3% for k-nearest neighbors tuning by grid search and 97.03% for k-nearest neighbors tuning by random search while the test accuracy of CNN based on grid search is 99.18% and for random search test Accuracy is 99.08 %.Finally, the test accuracy for decision trees based on grid search is 87.94% and for random search is 88.26%.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".