Categorical Data
Categorical data, also called qualitative data, is data whose values are discrete labels or groups (such as colors, country names, or blood types) rather than measurable quantities, so they cannot be…
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Continuous Feature
A continuous feature is a numeric input variable in machine learning and statistics that can take any value within a range, including decimals and fractions, rather than a fixed set of categories or counts.
Convenience Sampling
Convenience sampling (also called grab sampling, accidental sampling, or opportunity sampling) is a non-probability sampling method in which data points or participants are selected because they are easy to…
Inter-rater agreement
Inter-rater agreement is the degree of consensus among two or more independent raters when they label or score the same set of items.
Iris dataset
The Iris dataset, sometimes referred to as Fisher's Iris dataset or the Iris flower dataset, is a multivariate dataset introduced by the British statistician and biologist Ronald Fisher in his 1936 paper "The…
Non-Response Bias
Non-response bias is the error that arises when the people or units that do not respond to a survey, study, or data collection process differ systematically from those that do
Outlier Detection
Outlier detection is the process of identifying data points, observations, or patterns that deviate so markedly from the rest of a dataset that they are likely to have been generated by a different process.
Participation Bias
Participation bias is a systematic error that arises when the individuals who choose to take part in a study, survey, or data collection effort differ in meaningful ways from those who do not, so the resulting…
Sampling Bias
Sampling bias is a systematic error in statistics and machine learning that occurs when a sample is collected so that some members of the intended population have a higher or lower probability of being…
Selection Bias
Selection bias is a systematic error that occurs when the data used for analysis, training, or evaluation does not accurately represent the population or domain it is intended to describe