Supervised learning - is a machine learning task that establishes the mathematical relationship between input X and output Y variables.Machine learning algorithms could be broadly categorised to one of three types: Depending on the data type (qualitative or quantitative) of the target variable (commonly referred to as the Y variable) we are either going to be building a classification (if Y is qualitative) or regression (if Y is quantitative) model. Now, comes the fun part where we finally get to use the meticulously prepared data for model building. Finally, the metric values are based on the average performance computed from the 5 models. where each of the 5 folds have been left out as the testing set) where each of the 5 models contain associated performance metrics (which we will discuss soon in the forthcoming section). As a result, we will have built 5 models (i.e. This process is carried out iteratively until all folds had a chance to be left out as the testing data. The trained model is then applied on the aforementioned left-out fold ( i.e. In such N-fold CV, one of the fold is left out as the testing data while the remaining folds are used as the training data for model building.įor example, in a 5-fold CV, 1 fold is left out and used as the testing data while the remaining 4 folds are pooled together and used as the training data for model building.
In order to make the most economical use of the available data, an N-fold cross-validation (CV) is normally used whereby the dataset is partitioned to N folds ( i.e. Selection of the best model is made on the basis of the model’s performance on the testing set and in efforts to obtain the best possible model, hyperparameter optimization may also be performed. serving as the new, unseen data) to make predictions. Next, the training set is used to build a predictive model and such trained model is then applied on the testing set ( i.e. It should be noted that such data split is performed once. Particularly, the first portion is the larger data subset that is used as the training set (such as accounting for 80% of the original data) and the second is normally a smaller subset and used as the testing set (the remaining 20% of the data). In order to simulate the new, unseen data, the available data is subjected to data splitting whereby it is split to 2 portions (sometimes referred to as the train-test split).
In the development of machine learning models, it is desirable that the trained model perform well on new, unseen data.
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