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Results by Pipeline

We break down the base results here by pipeline (instead of parcellation type) in two different ways: Intra and Inter pipeline (corresponding to the top and bottom of the figure below). If necessary first see the intro to results page for a guide on how the results in this project are interpreted.

By Pipeline

Intra-Pipeline Comparison

When comparing in an intra-pipeline fashion, we are essentially computing the ranks independently for each choice of ML Pipeline. We also estimate the powerlaw region separately for each.

We can then model these results as log10(Mean_Rank) ~ log10(Size) * C(Pipeline) where Pipeline (the type of ML pipeline) is a fixed effect and can interact with Size (Fullscreen Plot Link).

OLS Regression Results
Dep. Variable: Mean_Rank R-squared: 0.882
Model: OLS Adj. R-squared: 0.881
Method: Least Squares F-statistic: 878.8
Date: Mon, 03 Jan 2022 Prob (F-statistic): 2.48e-270

coef std err t P>|t| [0.025 0.975]
Intercept 2.5893 0.019 135.246 0.000 2.552 2.627
C(Pipeline)[T.LGBM] -0.0208 0.026 -0.795 0.427 -0.072 0.031
C(Pipeline)[T.SVM] 0.3020 0.028 10.939 0.000 0.248 0.356
Size -0.2606 0.009 -30.318 0.000 -0.278 -0.244
Size:C(Pipeline)[T.LGBM] 0.0162 0.012 1.405 0.160 -0.006 0.039
Size:C(Pipeline)[T.SVM] -0.1291 0.012 -10.957 0.000 -0.152 -0.106

The resulting statistical table is a little bit difficult to make sense of at first, so let’s also plot the fit to the data to get a better feel.

By Pipeline

These results indicate that there are differences between the pipelines (i.e., scaling coefficient, range of scaling and intercept), as well as confirm more generally that scaling, albeit with varying degree, holds regardless of pipeline.

Another interesting way to view how results change when computed separately between pipelines is through an interactive visualization. Click Here for a fullscreen version of the plot.