Training Set
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In supervised or unsupervised learning, the training set is usually presented in a tabular format.
An example is the diabetes dataset, which is used to predict whether a person has diabetes. It contains records of 768 females with parameters such as age, body mass index, and blood pressure. These parameters are referred to as features.
The dataset also includes an 'Outcome' column indicating whether the person has diabetes. This is the target variable.
Each row in the table is an instance (also called a data point or sample), representing information about a single individual.
The table (training set) has a target column in it, which means it is labeled.
The task is to train the ML model on this training set, and once it is trained, it can predict for other people (new instances) whether they have diabetes based on features only.
This training set is an example of a biased dataset as it exclusively contains information about females who are at least 21 years old. Therefore, the model may produce less accurate predictions for males or for females under 21, since it hasn't been trained on these groups.
While coding, feature columns are usually assigned to X and target columns assigned as y.
And features of new instances are assigned as X_new.
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