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Fig. 2 | BMC Biology

Fig. 2

From: Machine learning approaches identify male body size as the most accurate predictor of species richness 

Fig. 2

Histogram of the percent of total variance each MCA dimension is able to explain. MCA recovers the total of eight dimensions to explain the total variance contained in the dataset. However, to be able to visually interpret the results, we must reduce the number of dimensions. An interpretable graphic representation of MCA results best operates in two dimensions. Therefore, we relied on the first two MCA dimensions for investigating variable relationships. Those two dimensions together explain 52.9% of the total variance (inertia) within the data while the 47.1% of the variance is lost

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BMC Biology

ISSN: 1741-7007

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