K-Fold Cross Validation
Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. When a specific value for k is chosen, it may be used in place of k in the reference to the model, such as k=10 becoming 10-fold cross-validation. Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model. It is a popular method because it is simple to understand and because it generally results in a less biased or less optimistic estimate of the model skill than other methods, such as a simple train/test split.
Applications of K-Fold Cross Validation? Let's Understand!
● improving model prediction
About K-Fold Cross Validation: Let's See!
●Tensorflow
●sci-kit learn
●Py torch
● Keras
●NumPy
●Pandas
●Matplotlib
●Theano
●Scipy
●Plotly
●Statsmodels
