Skip to main content

Advertisement

Figure 7 | BMC Bioinformatics

Figure 7

From: A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

Figure 7

The effect of different noise processes on the performance of a random forest (green triangles) and a PLS classification (red circles). In the left column, feature vectors are augmented by a random variable, which is subsequently rescaled according to a factor S (horizontal axis), thus introducing non-discriminatory variance to the classification problem. In the right column, a random variable scaled by factor S is added as constant offset to the feature vectors, increasing the correlation between features (see text for details). Shown are results on the basis of the bivariate classification problem of Fig. 1 (top row), the NMR candida 2 data (middle), and the BSE binned data (below).

Back to article page