Result Detail – Random Forest Help

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Result Information
Result ID
151
Type
Random Forest
Status
completed
Dataset ID
719
Created
2026-09-21 02:17:48.827397
Saved Model
Available — Download .pkl
Model Parameters
Model Type
regression
Estimators
60
Max Depth
None
Pre-Modeling Statistics (Pearson, Spearman, ANOVA) Help
Feature Pearson r Pearson p Pearson FDR Spearman ρ Spearman FDR ANOVA F ANOVA FDR Significant?
Protein_A01 0.0602 0.8010 0.8722 0.0962 0.9807 0.0654 0.8722 ✗ No
Protein_B02 -0.0739 0.7570 0.8722 -0.0105 0.9849 0.0987 0.8722 ✗ No
Protein_C03 -0.5620 0.0099 0.0591 -0.5233 0.1177 8.3084 0.0591 ✗ No
Protein_D04 0.0401 0.8667 0.8722 -0.0045 0.9849 0.0290 0.8722 ✗ No
Protein_E05 -0.0983 0.6802 0.8722 -0.1263 0.9807 0.1755 0.8722 ✗ No
Protein_F06 -0.5386 0.0143 0.0591 -0.5038 0.1177 7.3537 0.0591 ✗ No
Protein_G07 -0.2090 0.3766 0.7532 -0.2722 0.6142 0.8218 0.7532 ✗ No
Protein_H08 0.0384 0.8722 0.8722 0.0602 0.9849 0.0266 0.8722 ✗ No
Protein_I09 -0.5239 0.0177 0.0591 -0.3925 0.2899 6.8107 0.0591 ✗ No
Protein_J10 -0.2396 0.3090 0.7532 -0.2391 0.6200 1.0962 0.7532 ✗ No
Download CSV
Feature Importance (Random Forest) Help
Rank Feature Importance (Mean) SD
1 Protein_F06 0.2988 0.0735
2 Protein_I09 0.1481 0.0270
3 Protein_J10 0.1285 0.0840
4 Protein_C03 0.1149 0.0166
5 Protein_E05 0.0872 0.0683
6 Protein_G07 0.0667 0.0339
7 Protein_B02 0.0563 0.0133
8 Protein_A01 0.0458 0.0181
9 Protein_H08 0.0320 0.0217
10 Protein_D04 0.0218 0.0058
Download CSV
Permutation Importance Help
Rank Feature Perm. Importance (Mean) SD
1 Protein_F06 0.1945 0.2650
2 Protein_I09 0.1922 0.1650
3 Protein_J10 0.1712 0.1825
4 Protein_C03 0.1688 0.1437
5 Protein_G07 0.1142 0.1712
6 Protein_A01 0.0137 0.0338
7 Protein_H08 0.0081 0.0092
8 Protein_D04 -0.0137 0.0084
9 Protein_B02 -0.0147 0.0365
10 Protein_E05 -0.0826 0.0891
Download CSV
SHAP Feature Importance Help
Rank Feature SHAP Mean |Abs|
1 Protein_F06 0.4343
2 Protein_J10 0.2639
3 Protein_I09 0.2489
4 Protein_C03 0.2472
5 Protein_G07 0.2188
6 Protein_A01 0.1046
7 Protein_B02 0.0368
8 Protein_E05 0.0305
9 Protein_H08 0.0237
10 Protein_D04 0.0188
Download CSV
Model Metrics
Split R² MSE
Split 1 -0.9580 4.9474
Split 2 0.2923 3.2985
Split 3 -1.0692 2.3126
Mean -0.5783 3.5195
Download CSV
Figures
Feature importance Download PNG
Feature importance
Permutation importance Download PNG
Permutation importance
SHAP summary Download PNG
SHAP summary
Observed vs predicted (out-of-fold) Download PNG
Observed vs predicted (out-of-fold)
Cross-validation score distribution Download PNG
Cross-validation score distribution
Pathway Analysis (same dataset) Help

A pathway analysis was previously run on the same source dataset — it is not derived from this result.

View Pathway Results →

Method & Provenance

Method
Random Forest (Regression)
Method key
random_forest (result type: random_forest, result #151)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#719 gallery — example_proteomics.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/a55dc899cd514343aea98c71daeb65ee Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:17:48.827397

Parameters used

ParameterValue
task_type regression
model_type regression
target_column Protein_content
n_estimators 60
n_splits 3
test_size 0.25
run_univariate True
run_permutation True
run_shap True
permutation_repeats 10
fdr_threshold 0.05

Execution trace

Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made. 

from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=60, random_state=42)
scores = sklearn.model_selection.cross_val_score(model, X, y, cv=3)