The data to which a ML algorithm is applied is called a training set, which consists of a set of pairs (x, y), called training examples. The pairs are explained as follows:
x: This is a vector of values, often called the feature vector. Each value, or feature, can be categorical (values are taken from a set of discrete values, such as{S, M, L}) or numerical.y: This is the label, the classification or regression values forx.
The objective of the ML process is to discover a function that best predicts the value of
y associated with each value of x. The type of y is in principle arbitrary, but there are several common and important cases.
y: This is a real number. The ML problem is called regression.y: This is a Boolean value true or false, more commonly written as +1 and -1, respectively. In this class, the problem is binary classification.y: Here this is a member of some finite set. The member of this set can be thought of as classes, and each member represents one class. The problem is multiclass classification.y: This is a member of some potentially infinite set, for example, a parse tree forx, which is interpreted as a sentence.
Until now, machine learning has not proved successful in situations where we can describe the goals of the mining more directly. Machine learning and data mining are two different topics, although some algorithms are shared between them—algorithms are shared especially when the goal is to extract information. There are situations where machine learning makes sense. The typical one is when we have idea of what we looking for in the dataset.
The major classes of algorithms are listed here. Each is distinguished by the function .
- Decision tree: This form of
is a tree and each node of the tree has a function of
xthat determines which child or children the search must proceed for. - Perceptron: These are threshold functions applied to the components of the vector
- Neural nets: These are acyclic networks of perceptions, with the outputs of some perceptions used as inputs to others.
- Instance-based learning: This uses the entire training set to represent the function
- Support-vector machines: The result of this class is a classifier that tends to be more accurate on unseen data. The target for class separation denotes as looking for the optimal hyper-plane separating two classes by maximizing the margin between the classes' closest points.
The data aspects of machine learning here means the way data is handled and the way it is used to build the model.
- Training and testing: Assuming all the data is suitable for training, separate out a small fraction of the available data as the test set; use the remaining data to build a suitable model or classifier.
- Batch versus online learning: The entire training set is available at the beginning of the process for batch mode; the other one is online learning, where the training set arrives in a stream and cannot be revisited after it is processed.
- Feature selection: This helps to figure out what features to use as input to the learning algorithm.
- Creating a training set: This helps to create the label information that turns data into a training set by hand.