Is it correct that initial dataset can be spit into three main subsets: the training set, the validation set (to fine-tune parameters), and the testing set (checking performance on unseen data)?
It is indeed correct that the initial dataset in machine learning can be divided into three main subsets: the training set, the validation set, and the testing set. These subsets serve specific purposes in the machine learning workflow and play a crucial role in developing and evaluating models. The training set is the largest subset
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Why is it important to preprocess the dataset before training a CNN?
Preprocessing the dataset before training a Convolutional Neural Network (CNN) is of utmost importance in the field of artificial intelligence. By performing various preprocessing techniques, we can enhance the quality and effectiveness of the CNN model, leading to improved accuracy and performance. This comprehensive explanation will delve into the reasons why dataset preprocessing is crucial
What is the purpose of shuffling the dataset before splitting it into training and test sets?
Shuffling the dataset before splitting it into training and test sets serves a crucial purpose in the field of machine learning, particularly when applying one's own K nearest neighbors algorithm. This process ensures that the data is randomized, which is essential for achieving unbiased and reliable model performance evaluation. The primary reason for shuffling the