Can we use cross validation for feature selection?

Can we use cross validation for feature selection?

Cross-validation(CV) is the most commonly used method for model evaluation in feature selection. Suppose that there are m samples in the dataset used to build the model and they are usually divided into two parts, training set mtr and test set mte = m − mtr.

What is the difference between filter wrapper and embedded methods for feature selection?

The main differences between the filter and wrapper methods for feature selection are: Filter methods measure the relevance of features by their correlation with dependent variable while wrapper methods measure the usefulness of a subset of feature by actually training a model on it.

What is cross validation in feature selection?

Cross validation is a commonly used method for model evaluation when the amount of training data is limited. In the cross validation , the overall training data is divided into k segments. The model is trained using (k-1) segments and performance(Eg: R2 score, Mean square error) is evaluated with held out segment.

Is PCA a wrapper method?

Though PCA and Genetic based methods are applied for feature selection, Rough set based feature selection methods provide good results for many data sets. You can also try social impact theory based optimizer and opinion dynamics based optimizer for feature subset selection. These are wrapper based approaches.

What is K fold cross validation used for?

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.

Is cross validation necessary?

In general cross validation is always needed when you need to determine the optimal parameters of the model, for logistic regression this would be the C parameter.

What is wrapper based feature selection?

In wrapper methods, the feature selection process is based on a specific machine learning algorithm that we are trying to fit on a given dataset. Finally, it selects the combination of features that gives the optimal results for the specified machine learning algorithm.

What is wrapper approach?

The wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. In addition, the feature subsets selected by the wrapper are significantly smaller than the original subsets used by the learning algorithms, thus producing more comprehensible models.

What is N fold cross-validation?

N-fold cross validation, as i understand it, means we partition our data in N random equal sized subsamples. A single subsample is retained as validation for testing and the remaining N-1 subsamples are used for training. The result is the average of all test results.

Is k-fold cross-validation used for Hyperparameter tuning?

The k-fold cross-validation procedure is used to estimate the performance of machine learning models when making predictions on data not used during training. This procedure can be used both when optimizing the hyperparameters of a model on a dataset, and when comparing and selecting a model for the dataset.

What is N fold cross validation?

What is Monte Carlo cross validation?

Monte Carlo cross-validation (MCCV) simply splits the N data points into the two subsets nt and nv by sampling, without replacement, nt data points. The model is then trained on subset nt and validated on subset nv. There exist (Nnt) unique training sets, but MCCV avoids the need to run this many iterations.