Cross-validation is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it.

The number of folds is usually determined by the number of instances contained in your dataset. For example, if you have 10 instances in your data, 10-fold cross-validation wouldn’t make sense.

What is K-Fold Cross Validation? K-Fold CV is where a given data set is split into a K number of sections/folds where each fold is used as a testing set at some point. Lets take the scenario of 5-Fold cross validation(K=5). Here, the data set is split into 5 folds.

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.

K-Folds Cross Validation: Because it ensures that every observation from the original dataset has the chance of appearing in training and test set. This is one among the best approach if we have a limited input data. Repeat this process until every K-fold serve as the test set.

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Computing cross-validated metrics When the cv argument is an integer, cross_val_score uses the KFold or StratifiedKFold strategies by default, the latter being used if the estimator derives from ClassifierMixin .

The algorithm of k-Fold technique:

1. Pick a number of folds – k.
2. Split the dataset into k equal (if possible) parts (they are called folds)
3. Choose k – 1 folds which will be the training set.
4. Train the model on the training set.
5. Validate on the test set.
6. Save the result of the validation.
7. Repeat steps 3 – 6 k times.

K value indicates the count of the nearest neighbors. We have to compute distances between test points and trained labels points. Updating distance metrics with every iteration is computationally expensive, and that’s why KNN is a lazy learning algorithm.

2 Answers. K-fold cross validation is a standard technique to detect overfitting. It cannot “cause” overfitting in the sense of causality. However, there is no guarantee that k-fold cross-validation removes overfitting.

You don’t use cross validation when you’re doing the final test of your selected and tuned model. You should have separate training and testing data and cross-validation should only happen within the training data-set, typically for model selection and parameter tuning.

increased test set error. There are several approaches to avoiding overfitting in building decision trees. Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree.

Disadvantages of decision trees: They are unstable, meaning that a small change in the data can lead to a large change in the structure of the optimal decision tree. They are often relatively inaccurate. Many other predictors perform better with similar data.

Handling overfitting

1. Reduce the network’s capacity by removing layers or reducing the number of elements in the hidden layers.
2. Apply regularization , which comes down to adding a cost to the loss function for large weights.
3. Use Dropout layers, which will randomly remove certain features by setting them to zero.

Underfitting occurs when a model is too simple — informed by too few features or regularized too much — which makes it inflexible in learning from the dataset. Simple learners tend to have less variance in their predictions but more bias towards wrong outcomes.

Techniques to reduce underfitting :

1. Increase model complexity.
2. Increase number of features, performing feature engineering.
3. Remove noise from the data.
4. Increase the number of epochs or increase the duration of training to get better results.

Gradient boosting is a greedy algorithm and can overfit a training dataset quickly. It can benefit from regularization methods that penalize various parts of the algorithm and generally improve the performance of the algorithm by reducing overfitting.