In this section, we will describe the different concepts and terms normally used in machine learning:
Data or dataset: The basics of machine learning rely on understanding the data. The data or dataset normally refers to content available in structured or unstructured format for use in machine learning. Structured datasets have specific formats, and an unstructured dataset is normally in the form of some free-flowing text. Data can be available in various storage types or formats. In structured data, every element known as an instance or an example or row follows a predefined structure. Data can also be categorized by size: small or medium data have a few hundreds to thousands of instances, whereas big data refers to a large volume, mostly in millions or billions, that cannot be stored or accessed using common devices or fit in the memory of such devices.
Features, attributes, variables, or dimensions: In structured datasets, as mentioned before, there are predefined elements with their own semantics and data type, which are known variously as features, attributes, metrics, indicators, variables, or dimensions.
Data types: The features defined earlier need some form of typing in many machine learning algorithms or techniques. The most commonly used data types are as follows:
Categorical or nominal: This indicates well-defined categories or values present in the dataset. For example, eye color—black, blue, brown, green, grey; document content type—text, image, video.
Continuous or numeric: This indicates a numeric nature of the data field. For example, a person's weight measured by a bathroom scale, the temperature reading from a sensor, or the monthly balance in dollars on a credit card account.
Ordinal: This denotes data that can be ordered in some way. For example, garment size—small, medium, large; boxing weight classes: heavyweight, light heavyweight, middleweight, lightweight, and bantamweight.
Target or label: A feature or set of features in the dataset, which is used for learning from training data and predicting in an unseen dataset, is known as a target or a label. The term "ground truth" is also used in some domains. A label can have any form as specified before, that is, categorical, continuous, or ordinal.
Machine learning model: Each machine learning algorithm, based on what it learned from the dataset, maintains the state of its learning for predicting or giving insights into future or unseen data. This is referred to as the machine learning model.
Sampling: Data sampling is an essential step in machine learning. Sampling means choosing a subset of examples from a population with the intent of treating the behavior seen in the (smaller) sample as being representative of the behavior of the (larger) population. In order for the sample to be representative of the population, care must be taken in the way the sample is chosen. Generally, a population consists of every object sharing the properties of interest in the problem domain, for example, all people eligible to vote in the general election, or all potential automobile owners in the next four years. Since it is usually prohibitive (or impossible) to collect data for all the objects in a population, a well-chosen subset is selected for the purpose of analysis. A crucial consideration in the sampling process is that the sample is unbiased with respect to the population. The following are types of probability-based sampling:
Uniform random sampling: This refers to sampling that is done over a uniformly distributed population, that is, each object has an equal probability of being chosen.
Stratified random sampling: This refers to the sampling method used when the data can be categorized into multiple classes. In such cases, in order to ensure all categories are represented in the sample, the population is divided into distinct strata based on these classifications, and each stratum is sampled in proportion to the fraction of its class in the overall population. Stratified sampling is common when the population density varies across categories, and it is important to compare these categories with the same statistical power. Political polling often involves stratified sampling when it is known that different demographic groups vote in significantly different ways. Disproportional representation of each group in a random sample can lead to large errors in the outcomes of the polls. When we control for demographics, we can avoid oversampling the majority over the other groups.
Cluster sampling: Sometimes there are natural groups among the population being studied, and each group is representative of the whole population. An example is data that spans many geographical regions. In cluster sampling, you take a random subset of the groups followed by a random sample from within each of those groups to construct the full data sample. This kind of sampling can reduce the cost of data collection without compromising the fidelity of distribution in the population.
Systematic sampling: Systematic or interval sampling is used when there is a certain ordering present in the sampling frame (a finite set of objects treated as the population and taken to be the source of data for sampling, for example, the corpus of Wikipedia articles, arranged lexicographically by title). If the sample is then selected by starting at a random object and skipping a constant k number of objects before selecting the next one, that is called systematic sampling. The value of k is calculated as the ratio of the population to the sample size.
Model evaluation metrics: Evaluating models for performance is generally based on different evaluation metrics for different types of learning. In classification, it is generally based on accuracy, receiver operating characteristics (ROC) curves, training speed, memory requirements, false positive ratio, and so on, to name a few (see Chapter 2, Practical Approach to Real-World Supervised Learning). In clustering, the number of clusters found, cohesion, separation, and so on form the general metrics (see Chapter 3, Unsupervised Machine Learning Techniques). In stream-based learning, apart from the standard metrics mentioned earlier, adaptability, speed of learning, and robustness to sudden changes are some of the conventional metrics for evaluating the performance of the learner (see Chapter 5, Real-Time Stream Machine Learning).
To illustrate these concepts, a concrete example in the form of a commonly used sample weather dataset is given. The data gives a set of weather conditions and a label that indicates whether the subject decided to play a game of tennis on the day or not:
@relation weather @attribute outlook {sunny, overcast, rainy} @attribute temperature numeric @attribute humidity numeric @attribute windy {TRUE, FALSE} @attribute play {yes, no} @data sunny,85,85,FALSE,no sunny,80,90,TRUE,no overcast,83,86,FALSE,yes rainy,70,96,FALSE,yes rainy,68,80,FALSE,yes rainy,65,70,TRUE,no overcast,64,65,TRUE,yes sunny,72,95,FALSE,no sunny,69,70,FALSE,yes rainy,75,80,FALSE,yes sunny,75,70,TRUE,yes overcast,72,90,TRUE,yes overcast,81,75,FALSE,yes rainy,71,91,TRUE,no
The dataset is in the format of an ARFF (attribute-relation file format) file. It consists of a header giving the information about features or attributes with their data types and actual comma-separated data following the data tag. The dataset has five features, namely outlook, temperature, humidity, windy, and play. The features outlook and windy are categorical features, while humidity and temperature are continuous. The feature play is the target and is categorical.