লক্ষ্য করুন: এই এন্ট্রির অনুবাদ বর্তমানে মান পর্যালোচনার অধীনে রয়েছে, তাই কিছু বিষয়বস্তু সাময়িকভাবে শুধুমাত্র ইংরেজিতে প্রদর্শিত হচ্ছে।
এই এন্ট্রিটি এখনও আপনার ভাষায় অনুবাদ করা হয়নি, তাই নিচে মূল লেখাটি দেখানো হচ্ছে।
cross validation
This term is a technical staple of data science and statistics, specifically used to describe the process of assessing how a result will generalize to an independent data set. It carries a connotation of rigor and reliability, as it is the primary defense against overfitting, where a model learns noise rather than the underlying pattern.
In professional contexts, it is often used as a shorthand for the specific k-fold method, though other variations like leave-one-out exist. It is almost exclusively used in academic, technical, or corporate data-driven environments and would be out of place in general conversation.
Meanings
A technique in machine learning and statistics used to evaluate the performance of a predictive model by partitioning a dataset into subsets, training the model on one subset, and validating it on the remaining subset to ensure the model generalizes well to unseen data.
The researcher used k-fold cross validation to ensure the model was not overfitting the training data.