Random Forest run #151 · random_forest

Table view Dataset report
-0.5783
r2 mean
regression
task type
0.6173
r2 std
3.52
mse mean
completed
status
2026-09-21 02:17
created
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
Permutation Importance 10 rows
namemeanstd
Protein_A010.0137449191625511530.03376730978712609
Protein_B02-0.0146932613376651260.03650583544771049
Protein_C030.168845885341872060.1436591451560098
Protein_D04-0.0137200370830481720.008410988824748687
Protein_E05-0.082586573057409250.0890676994060704
Protein_F060.194496032871182420.2649822527075082
Protein_G070.114167448907968190.17121416782783516
Protein_H080.0081020349813551150.009231680939295443
Protein_I090.192245947151443940.16495687517176325
Protein_J100.1712055287052960.18252466884069643
Shap Importance 10 rows
namemean_abs
Protein_A010.1045723922861332
Protein_B020.03677605023727556
Protein_C030.24716498509949342
Protein_D040.01880241105418062
Protein_E050.030456671039570053
Protein_F060.4342805614278148
Protein_G070.21884891221432162
Protein_H080.02372534572446395
Protein_I090.24887702123911942
Protein_J100.26387881983536676
Split Metrics 3 rows
r2mse
-0.9580217300408144.947416912491169
0.2922926541750653.2984713262759606
-1.0691723189277462.3126256985883273
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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)