Terminology Related to Machine Learning

Terminology Related to Machine Learning Machine learning has become a crucial component of many different sectors and professions, ranging from banking to healthcare and everything in between. It is hardly surprising that machine learning is quickly gaining popularity given its capacity to analyze and learn from enormous volumes of data. Yet, it is crucial to first comprehend machine learning's language in order to apply and comprehend it successfully. In this article, we will go over some of the basic concepts and terminology related to machine learning. By the end of this article, you will be able to describe what a feature is, how it relates to a sample, and differentiate between a categorical feature and a numerical feature. Additionally, we will discuss the various terms used to describe data, samples, and variables in machine learning. What is a Sample? A sample refers to an instance or example of an entity in your dataset. Typically, a sample is represented as a row in your dataset. For instance, weather data for a particular day can be considered as a sample. A dataset can consist of numerous samples, each with several values associated with it. What are Variables? Variables, often known as aspects of the sample, are several pieces of information about a sample. Several variables may be present in each sample. For instance, the sample of meteorological data for a specific day may include elements like that date's minimum temperature, highest temperature, and amount of rainfall. Samples and variables are referred to in machine learning by a variety of terms, including record, example, row, instance, observation, feature, column, dimension, attribute, and field. What are Numeric Variables? Numeric variables are those that take on numerical values, which can be measured and sorted in some order. Numeric variables can be continuous or discrete and have positive, negative, or both types of values. For instance, age is a numeric variable that can have any real value. What are Categorical Variables? Categorical variables are those that have labels, names, or categories for values, instead of numbers. They describe some quality or characteristic of an entity. For example, the color of a car can be described as red, silver, blue, or black. These values are non-numeric and can be sorted into categories. Categorical variables are also known as qualitative variables or nominal variables. Data Types Each variable has a data type associated with it. The most common data types in machine learning are numeric and categorical. However, there are other data types as well, such as string and date. Numeric variables can have integer or continuous values, while categorical variables have labels or categories as values. To Summarize In summary, a sample is an instance or example of an entity in your data, and a variable captures specific characteristics of each entity. A sample can have multiple variables to describe it, and the data from real applications are often multidimensional. Each variable has a data type associated with it, and the most common data types are numeric and categorical. In conclusion, it is essential to comprehend the fundamental terms and principles of machine learning in order to utilize and comprehend it correctly. You can more effectively understand the complexities of machine learning and analyze and interpret data if you have a thorough understanding of the numerous terminologies used to describe data, samples, and variables.