Hyperparameter Optimization for CNN, K-NN, and Decision Tree in Handwritten Digit Classification

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%.