Variance

Q: How deeply does Delivering Machine Learning Projects: From design to deployment cover variance?

Connects dataset variance to validation-set sizing and training order, without detailing calculation methods.

As explained in Delivering Machine Learning Projects: From design to deployment

Variance is a statistical measure of the average squared deviation of values from their mean.

Variance measures how widely key data properties are spread around their typical values. In exploratory data analysis, that spread helps determine whether validation data will represent the target domain well. section 6.1.1

Use variance as an EDA signal for modelling decisions:

Estimate validation-set size from the variance of important dataset properties. section 6.1.1
Use the spread of key variables to inform how data is ordered during training. section 6.1.1

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Related concepts

principal component analysisused by · Principal component analysis is a linear dimensionality-reduction technique that transforms data into orthogonal components ordered by the variance they capture.
standard deviationused by · Standard deviation is the nonnegative square root of variance and measures the typical dispersion of values around their mean in the original units.
data aggregationaddressed by · Data aggregation is the combination of multiple values, records, observations, or results into a consolidated or summarized output.
meanuses · A mean is a measure of central tendency, calculated as the arithmetic average of values or, for a random variable, as the probability-weighted average of its possible values.
Cramer-Rao lower boundmeasured by · The Cramér-Rao lower bound is a theoretical lower limit on the variance of unbiased estimators under specified regularity conditions and a probability model.
biascontrasts with · Bias is a systematic deviation or tendency in data, measurements, estimates, judgments, or outputs that skews them relative to a relevant target or impartial baseline.

This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.