Difference between homonyms and synonyms in data science with examples

Homonyms- are words that have the same sound but have different in meaning. 2. Synonyms- are words that have the same or almost the same meaning.

Homonyms

Machine learning algorithms are now the subject of ethical debate. Bias, in layman's terms, is a pre-formed view created before facts are known. It applies to an estimating procedure's proclivity to provide estimations or predictions that are, on average, off goal in machine learning and data mining.

A policy's strength can be measured in a variety of ways, including confidence. "Decision trees" are diagrams that show how decisions are being made and what consequences are available. Rescale a statistic to match the scale of other variables in the model to normalise it.

Confidence is a statistician's metric for determining how reliable a sample is (we are 95 percent confident that the average blood sugar in the group lies between X and Y, based on a sample of N patients). Decision tree algorithms are methods that divide data across pieces that are becoming more and more homogeneous in terms of the outcome measure as they advance.

A graph is a graphical representation of data that statisticians call plots and charts. A graph seems to be an information structure that contains the ties and links among items, according to computer programmers. The act of arranging relational databases and their columns such that table relationships are consistent is known as normalisation.

Synonyms

Statisticians use the terms record, instance, sample, or example to describe their data. In computer science and machine learning, this can be called an attribute, input variable, or feature. The term "estimation" is also used, though its use is generally limited to numeric outcomes.

Statisticians call the non-time-series data format a record, or record. In statistics, estimation more often refers to the use of a sample statistic to measure something. Predictive modelling involves developing aggregations of low-level predictors into more informative "features".

The spreadsheet format, in which each column is still a variable, so each row is a record, is perhaps the most common non-time-series data type. Modeling in machine learning and artificial intelligence often begins with some very low-level prediction data.