Table of Contents
Is overfitting possible in cross-validation?
Cross-Validation is a good, but not perfect, technique to minimize over-fitting. Cross-Validation will not perform well to outside data if the data you do have is not representative of the data you’ll be trying to predict!
What is true about cross-validation?
Cross-validation is a technique in which we train our model using the subset of the data-set and then evaluate using the complementary subset of the data-set. The three steps involved in cross-validation are as follows : Reserve some portion of sample data-set. Using the rest data-set train the model.
How can overfitting be detected?
Overfitting can be identified by checking validation metrics such as accuracy and loss. The validation metrics usually increase until a point where they stagnate or start declining when the model is affected by overfitting.
Does cross validation reduce Type 2 error?
In the context of building a predictive model, I understand that cross validation (such as K-Fold) is a technique to find the optimal hyper-parameters in reducing bias and variance somewhat. Recently, I was told that cross validation also reduces type I and type II error.
Why cross validation is most accurate evaluation technique in classification?
Cross validation approach to report the result assures unbiased result. In cross validation approach the data used for training and testing are non-overlapping and there by test results which are usually reported are not biased.
Is high bias overfitting?
A model that exhibits small variance and high bias will underfit the target, while a model with high variance and little bias will overfit the target. A model with high variance may represent the data set accurately but could lead to overfitting to noisy or otherwise unrepresentative training data.
Does cross validation reduce bias?
This significantly reduces bias as we are using most of the data for fitting, and also significantly reduces variance as most of the data is also being used in validation set.
Does cross validation reduce error?
Cross-validation is a good technique to test a model on its predictive performance. While a model may minimize the Mean Squared Error on the training data, it can be optimistic in its predictive error.
Why is cross validation a better choice for testing?
Cross-Validation is a very powerful tool. It helps us better use our data, and it gives us much more information about our algorithm performance. In complex machine learning models, it’s sometimes easy not pay enough attention and use the same data in different steps of the pipeline.