How is nested cross validation used in scikit-learn?
Nested Cross-Validation With Scikit-Learn It is common to evaluate machine learning models on a dataset using k-fold cross-validation. The k-fold cross-validation procedure divides a limited dataset into k non-overlapping folds.
When to use McNemar’s test to compare classifiers?
In his important and widely cited 1998 paper on the use of statistical hypothesis tests to compare classifiers titled “ Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms “, Thomas Dietterich recommends the use of the McNemar’s test.
How does nested cross validation reduce the bias?
Repeat the process multiple times and average the validation error, we get an estimate of the generalization performance of the model. Since the test data is untouched during each training, we kind of use the whole dataset to estimate the generalization error, which will reduce the bias.
How to cross validation in Python for machine learning?
The k-fold cross-validation procedure is available in the scikit-learn Python machine learning library via the KFold class. The class is configured with the number of folds (splits), then the split () function is called, passing in the dataset.
Can you use confusion matrix in scikit learn?
You cannot do this with confusion matrix which, again as name suggests, is a matrix. If you want to obtain confusion matrices for multiple evaluation runs (such as cross validation) you have to do this by hand, which is not that bad in scikit-learn – it is actually a few lines of code.
How is confusion matrix different from cross validation?
You need to understand difference between cross_val_score and cross validation as model selection method. cross_val_score as name suggests, works only on scores. Confusion matrix is not a score, it is a kind of summary of what happened during evaluation.