Analyst: Jennifer Fouquier

EXPLANA Description and Analysis Summary

EXPLANA Description

EXPLANA uses established machine-learning methods combined with innovative techniques to identify the most relevant features, from a variety of input features, that correlate with a response variable.

Input features and response variables can be numerical, categorical, or from non-normal data distributions. The relationships between selected features and the response can be linear or complex, non-linear relationships.

For longitudinal datasets, changes in features for each study identifier, often subjects, are calculated using different reference points to obtain delta datasets (First, Previous and Pairwise delta datasets). This is important because features in longitudinal studies can carry varying degrees of importance between models built using different reference points. EXPLANA automates feature selection from several models built using these datasets. This report was generated to textually and graphically summarize exploratory analysis and aid hypothesis generation.

Please ensure you understand the workflow, parameters/decisions, and that the percent variation explained (using out-of-bag (OOB) scores) is adequate for your purposes.

When you are using data without prior hypotheses, you are performing exploratory analysis and should make this clear when communicating results.

Response Variable: Crude_Rate_per_100000

Analysis Notes:

Methods

Feel free to use the following text, including citation information, for use in methods to ensure reproducibility:

EXPLANA was used for exploratory analysis to identify important features related to the response variable, Crude_Rate_per_100000.

A random effect of State was used to adjust for non-independence (repeated measurements) if needed. There were 1000 trees used per Random Forest model with a max feature fraction of 0.2 of the input features for each split per decision tree in the forest. If mixed effects Random Forests were needed, 20 iterations were performed. BorutaSHAP was used to find features that perform repeatedly better than shuffled versions of all input features. Features were considered important if they performed better than 97% of the SHAP importance score of the best shuffled feature using 100 trials, p=0.05. Categorical variables were binary encoded and low occuring categorical values were not removed.

These methods are from an EXPLANA feature selection report (version: 2025.05.09) created on 2026-09-08. Additional information can be found at https://github.com/JTFouquier/explana/.

Config File

analyst: Jennifer Fouquier
response_var: Crude_Rate_per_100000
include_time: 'yes'
random_effect: State
sample_id: sample_id
timepoint: Year
out: workflow-results/EXPLANA-crc-Crude_Rate_per_100000-mixed-model-2018-2022-complete-columns-20260908/
iterations: '20'
n_estimators: '1000'
max_features: '0.2'
borutashap_trials: '100'
borutashap_threshold: '97'
borutashap_p: '0.05'
analyze_original: 'yes'
analyze_first: 'yes'
analyze_previous: 'yes'
analyze_pairwise: 'yes'
absolute_values: 'no'
include_reference_values: 'no'
analysis_notes: ''
enc_percent_threshold: '0'
distance_matrices: list()
df_mod: ''
delta_df_mod: |
  delta_df <- delta_df
input_datasets:
  metadata:
    file_path: /Users/jenniferfouquier/repos/snakemake-merf/data/crc/explana_colorectal_complete_case_columns.csv
    df_mod: |
      df <- df %>%
        select(-Total_Count_Known) %>%
        filter(Year %in% c(2018, 2020, 2022)) %>%
        select(
          -N_Age_Groups_Suppressed,
          -N_Age_Groups,
          -Current_Smoker_Status__No,
          -Alcohol_Consumption__No,
          -Binge_Drinking__No,
          -Diabetes__No,
          -Exercise__No,
          -Fair_or_Poor_Health__Fair_or_Poor_Health
          )
    dim_method: ''
    dim_param_dict:
      method: none

Results Summary

Selected Features by Dataset

Data Original First Previous Pairwise
% Variance Explained 40.8% (41.9%) 35.7% (33.5%) 12.5% (19.1%) 24.9% (24.6%)
N Trees 1000 1000 1000 1000
Feature fraction/split 0.2 0.2 0.2 0.2
Max Depth 7 7 7 7
MERF Iters. 20 20 20 20
BorutaSHAP Trials 100 100 100 100
BorutaSHAP Threshold 97 97 97 97
P-value 0.05 0.05 0.05 0.05
N Study IDs 51 51 51 51
N Samples 153 102 102 153
Input Features 21 22 22 23
Accepted Features 17 9 9 7
Tentative Features 1 1 0 2
Rejected Features 3 12 13 14
Model Type (Pass/Fail) MERF PASS; Boruta PASS MERF PASS; Boruta PASS MERF PASS; Boruta PASS MERF PASS; Boruta PASS

Selected feature ranks from models built using Original and, for longitudinal analyses, First, Previous and Pairwise delta datasets. Selected features are shown in black and labeled with feature rank. For true/positive instances of categorical variables (indicated with “ENC” after encoding), average impact on response is shown after the rank. For numerical features, impact is not shown because the feature relationship to response can be complex, requiring further post-hoc tests or inspection of SHAP dependence plots for additional insight. Empty grey boxes indicate features included in the model for a dataset, but not selected. Long feature names may be truncated and indicated with ellipses. A comprehensive list of input features can be found in model details below.

Original Dataset

⇨ Open File Directory

Model Summary

Data Original
Model Type (Pass/Fail) MERF PASS; Boruta PASS
% Variance Explained 40.8% (41.9%)
N Trees 1000
Feature fraction/split 0.2
Max Depth 7
MERF Iters. 20
BorutaSHAP Trials 100
BorutaSHAP Threshold 97
P-value 0.05
N Study IDs 51
N Samples 153
Input Features 21
Accepted Features 17
Tentative Features 1
Rejected Features 3

SHAP summary beeswarm plots of feature influence on the machine learning prediction of response values. Each point represents one sample, and the horizontal position indicates impact on the response as indicated on the x-axis. Points to the left indicate a negative impact, and points to the right indicate a positive impact. The colors represent the selected feature values, where red is larger and blue is smaller. For binary encoded features (‘ENC’) red is yes[1] and blue is no[0]. SHAP is generally an improvement upon other importance scores because it provides information about both rank (how helpful the feature was compared to other features [y-axis]) and impact (a positive or negative impact on response values [x-axis]). If multiple figures are shown, scales may vary with a maximum of ten features per plot.

Selected Features

important_features decoded_features feature_importance_vals
timepoint_explana timepoint_explana 0.3468
Diabetes__Yes Diabetes__Yes 0.2313
Total_Population Total_Population 0.2094
BMI_Categories__Obese_BMI_30_0_99_8 BMI_Categories__Obese_BMI_30_0_99_8 0.1757
BMI_Categories__Overweight_BMI_25_0_29_9 BMI_Categories__Overweight_BMI_25_0_29_9 0.1483
Overall_Health__Excellent Overall_Health__Excellent 0.1477
Overall_Health__Fair Overall_Health__Fair 0.1308
Fair_or_Poor_Health__Good_or_Better_Health Fair_or_Poor_Health__Good_or_Better_Health 0.1169
Overall_Health__Very_good Overall_Health__Very_good 0.1015
Alcohol_Consumption__Yes Alcohol_Consumption__Yes 0.0964
Smoker_Status__Smoke_some_days Smoker_Status__Smoke_some_days 0.0888
Smoker_Status__Smoke_everyday Smoker_Status__Smoke_everyday 0.0821
Current_Smoker_Status__Yes Current_Smoker_Status__Yes 0.0792
BMI_Categories__Normal_Weight_BMI_18_5_24_9 BMI_Categories__Normal_Weight_BMI_18_5_24_9 0.0787
Exercise__Yes Exercise__Yes 0.0598
Smoker_Status__Never_smoked Smoker_Status__Never_smoked 0.0530
Overall_Health__Poor Overall_Health__Poor 0.0405

Feature Stats

important_features feature_importance_vals mean std min 25% 50% 75% max
timepoint_explana 0.3468 2.000 0.819 1.0 1.0 2.0 3.0 3.0
Diabetes__Yes 0.2313 11.177 2.066 7.0 9.7 11.0 12.5 17.4
Total_Population 0.2094 2555839.804 2989127.369 219521.0 664567.0 1726078.0 3116970.0 16539408.0
BMI_Categories__Obese_BMI_30_0_99_8 0.1757 32.369 4.124 23.0 29.5 32.4 35.4 41.0
BMI_Categories__Overweight_BMI_25_0_29_9 0.1483 34.614 1.469 30.9 33.6 34.7 35.6 38.2
Overall_Health__Excellent 0.1477 18.863 3.199 12.4 16.7 18.7 20.7 28.6
Overall_Health__Fair 0.1308 12.331 2.295 8.1 10.6 12.2 13.9 17.8
Fair_or_Poor_Health__Good_or_Better_Health 0.1169 83.502 3.525 73.7 81.1 84.1 86.0 89.6
Overall_Health__Very_good 0.1015 33.330 2.796 26.3 31.3 33.6 35.5 39.3
Alcohol_Consumption__Yes 0.0964 52.850 7.038 29.7 49.5 53.5 58.0 67.4
Smoker_Status__Smoke_some_days 0.0888 4.380 0.817 2.5 3.8 4.4 4.9 6.9
Smoker_Status__Smoke_everyday 0.0821 10.807 2.896 4.2 8.7 10.6 13.0 20.6
Current_Smoker_Status__Yes 0.0792 15.195 3.361 6.7 12.8 15.1 17.4 25.2
BMI_Categories__Normal_Weight_BMI_18_5_24_9 0.0787 31.169 3.527 23.2 28.4 30.9 33.5 42.7
Exercise__Yes 0.0598 76.471 3.718 67.6 74.4 76.7 79.0 84.8
Smoker_Status__Never_smoked 0.0530 59.949 4.606 49.6 56.8 59.5 62.4 75.7
Overall_Health__Poor 0.0405 4.161 1.397 1.8 3.2 3.8 4.8 9.4

Input Features

input_features was_selected
Total_Population yes
Alcohol_Consumption__Yes yes
BMI_Categories__Normal_Weight_BMI_18_5_24_9 yes
BMI_Categories__Obese_BMI_30_0_99_8 yes
BMI_Categories__Overweight_BMI_25_0_29_9 yes
Binge_Drinking__Yes no
Current_Smoker_Status__Yes yes
Diabetes__No_pre_diabetes_or_borderline_diabetes no
Diabetes__Yes yes
Exercise__Yes yes
Fair_or_Poor_Health__Good_or_Better_Health yes
Overall_Health__Excellent yes
Overall_Health__Fair yes
Overall_Health__Good no
Overall_Health__Poor yes
Overall_Health__Very_good yes
Smoker_Status__Former_smoker no
Smoker_Status__Never_smoked yes
Smoker_Status__Smoke_everyday yes
Smoker_Status__Smoke_some_days yes
timepoint_explana yes

Interpretation/Literature Search

The following links can help with hypothesis generation. Names of variables likely need modification.

important_features url
timepoint_explana https://pubmed.ncbi.nlm.nih.gov/?term=timepoint_explana%20AND%20Crude_Rate_per_100000
Diabetes__Yes https://pubmed.ncbi.nlm.nih.gov/?term=Diabetes__Yes%20AND%20Crude_Rate_per_100000
Total_Population https://pubmed.ncbi.nlm.nih.gov/?term=Total_Population%20AND%20Crude_Rate_per_100000
BMI_Categories__Obese_BMI_30_0_99_8 https://pubmed.ncbi.nlm.nih.gov/?term=BMI_Categories__Obese_BMI_30_0_99_8%20AND%20Crude_Rate_per_100000
BMI_Categories__Overweight_BMI_25_0_29_9 https://pubmed.ncbi.nlm.nih.gov/?term=BMI_Categories__Overweight_BMI_25_0_29_9%20AND%20Crude_Rate_per_100000
Overall_Health__Excellent https://pubmed.ncbi.nlm.nih.gov/?term=Overall_Health__Excellent%20AND%20Crude_Rate_per_100000
Overall_Health__Fair https://pubmed.ncbi.nlm.nih.gov/?term=Overall_Health__Fair%20AND%20Crude_Rate_per_100000
Fair_or_Poor_Health__Good_or_Better_Health https://pubmed.ncbi.nlm.nih.gov/?term=Fair_or_Poor_Health__Good_or_Better_Health%20AND%20Crude_Rate_per_100000
Overall_Health__Very_good https://pubmed.ncbi.nlm.nih.gov/?term=Overall_Health__Very_good%20AND%20Crude_Rate_per_100000
Alcohol_Consumption__Yes https://pubmed.ncbi.nlm.nih.gov/?term=Alcohol_Consumption__Yes%20AND%20Crude_Rate_per_100000
Smoker_Status__Smoke_some_days https://pubmed.ncbi.nlm.nih.gov/?term=Smoker_Status__Smoke_some_days%20AND%20Crude_Rate_per_100000
Smoker_Status__Smoke_everyday https://pubmed.ncbi.nlm.nih.gov/?term=Smoker_Status__Smoke_everyday%20AND%20Crude_Rate_per_100000
Current_Smoker_Status__Yes https://pubmed.ncbi.nlm.nih.gov/?term=Current_Smoker_Status__Yes%20AND%20Crude_Rate_per_100000
BMI_Categories__Normal_Weight_BMI_18_5_24_9 https://pubmed.ncbi.nlm.nih.gov/?term=BMI_Categories__Normal_Weight_BMI_18_5_24_9%20AND%20Crude_Rate_per_100000
Exercise__Yes https://pubmed.ncbi.nlm.nih.gov/?term=Exercise__Yes%20AND%20Crude_Rate_per_100000
Smoker_Status__Never_smoked https://pubmed.ncbi.nlm.nih.gov/?term=Smoker_Status__Never_smoked%20AND%20Crude_Rate_per_100000
Overall_Health__Poor https://pubmed.ncbi.nlm.nih.gov/?term=Overall_Health__Poor%20AND%20Crude_Rate_per_100000

Log


Binary encoded columns created for categorical input variables:

BorutaSHAP Figures

⇨ Open PDF in new window

First Delta Dataset

⇨ Open File Directory

Results

Model Summary

Data First
Model Type (Pass/Fail) MERF PASS; Boruta PASS
% Variance Explained 35.7% (33.5%)
N Trees 1000
Feature fraction/split 0.2
Max Depth 7
MERF Iters. 20
BorutaSHAP Trials 100
BorutaSHAP Threshold 97
P-value 0.05
N Study IDs 51
N Samples 102
Input Features 22
Accepted Features 9
Tentative Features 1
Rejected Features 12

SHAP summary beeswarm plots of feature influence on the machine learning prediction of response values. Each point represents one sample, and the horizontal position indicates impact on the response as indicated on the x-axis. Points to the left indicate a negative impact, and points to the right indicate a positive impact. The colors represent the selected feature values, where red is larger and blue is smaller. For binary encoded features (‘ENC’) red is yes[1] and blue is no[0]. SHAP is generally an improvement upon other importance scores because it provides information about both rank (how helpful the feature was compared to other features [y-axis]) and impact (a positive or negative impact on response values [x-axis]). If multiple figures are shown, scales may vary with a maximum of ten features per plot.

Selected Features

important_features decoded_features feature_importance_vals
ENC_timepoint_explana_is_1__3 timepoint_explana 0.2963
ENC_timepoint_explana_is_1__2 timepoint_explana 0.2759
Current_Smoker_Status__Yes Current_Smoker_Status__Yes 0.1689
Smoker_Status__Smoke_everyday Smoker_Status__Smoke_everyday 0.1612
BMI_Categories__Overweight_BMI_25_0_29_9 BMI_Categories__Overweight_BMI_25_0_29_9 0.1475
Fair_or_Poor_Health__Good_or_Better_Health Fair_or_Poor_Health__Good_or_Better_Health 0.1473
Alcohol_Consumption__Yes Alcohol_Consumption__Yes 0.1081
Overall_Health__Poor Overall_Health__Poor 0.1070
BMI_Categories__Obese_BMI_30_0_99_8 BMI_Categories__Obese_BMI_30_0_99_8 0.0871

Feature Stats

important_features feature_importance_vals unique top freq mean std min 25% 50% 75% max
ENC_timepoint_explana_is_1__3 0.2963 2 False 51 NA NA NA NA NA NA NA
ENC_timepoint_explana_is_1__2 0.2759 2 True 51 NA NA NA NA NA NA NA
Current_Smoker_Status__Yes 0.1689 NA NA -2.1019608 1.2659267 -6.0 -2.975 -2.1 -1.2 0.9
Smoker_Status__Smoke_everyday 0.1612 NA NA -1.3480392 1.0308826 -4.4 -2.100 -1.2 -0.6 1.0
BMI_Categories__Overweight_BMI_25_0_29_9 0.1475 NA NA -0.3166667 1.4425008 -4.4 -1.175 -0.3 0.5 3.6
Fair_or_Poor_Health__Good_or_Better_Health 0.1473 NA NA 2.0411765 1.9685323 -1.9 0.400 2.2 3.4 6.6
Alcohol_Consumption__Yes 0.1081 NA NA -0.4186275 2.3540877 -10.3 -1.675 -0.5 1.2 5.3
Overall_Health__Poor 0.1070 NA NA -0.9372549 0.7593538 -3.5 -1.400 -0.8 -0.4 0.4
BMI_Categories__Obese_BMI_30_0_99_8 0.0871 NA NA 1.6058824 1.8171255 -2.3 0.375 1.5 2.7 6.7

Input Features

input_features was_selected
Total_Population no
Alcohol_Consumption__Yes yes
BMI_Categories__Normal_Weight_BMI_18_5_24_9 no
BMI_Categories__Obese_BMI_30_0_99_8 yes
BMI_Categories__Overweight_BMI_25_0_29_9 yes
Binge_Drinking__Yes no
Current_Smoker_Status__Yes yes
Diabetes__No_pre_diabetes_or_borderline_diabetes no
Diabetes__Yes no
Exercise__Yes no
Fair_or_Poor_Health__Good_or_Better_Health yes
Overall_Health__Excellent no
Overall_Health__Fair no
Overall_Health__Good no
Overall_Health__Poor yes
Overall_Health__Very_good no
Smoker_Status__Former_smoker no
Smoker_Status__Never_smoked no
Smoker_Status__Smoke_everyday yes
Smoker_Status__Smoke_some_days no
ENC_timepoint_explana_is_1__2 yes
ENC_timepoint_explana_is_1__3 yes

Log


Binary encoded columns created for categorical input variables:

 ->  timepoint_explana: ['ENC_timepoint_explana_is_1__2', 'ENC_timepoint_explana_is_1__3']

BorutaSHAP Figures

⇨ Open PDF in new window

Previous Delta Dataset

⇨ Open File Directory

Model Summary

Data Previous
Model Type (Pass/Fail) MERF PASS; Boruta PASS
% Variance Explained 12.5% (19.1%)
N Trees 1000
Feature fraction/split 0.2
Max Depth 7
MERF Iters. 20
BorutaSHAP Trials 100
BorutaSHAP Threshold 97
P-value 0.05
N Study IDs 51
N Samples 102
Input Features 22
Accepted Features 9
Tentative Features 0
Rejected Features 13

SHAP summary beeswarm plots of feature influence on the machine learning prediction of response values. Each point represents one sample, and the horizontal position indicates impact on the response as indicated on the x-axis. Points to the left indicate a negative impact, and points to the right indicate a positive impact. The colors represent the selected feature values, where red is larger and blue is smaller. For binary encoded features (‘ENC’) red is yes[1] and blue is no[0]. SHAP is generally an improvement upon other importance scores because it provides information about both rank (how helpful the feature was compared to other features [y-axis]) and impact (a positive or negative impact on response values [x-axis]). If multiple figures are shown, scales may vary with a maximum of ten features per plot.

Selected Features

important_features decoded_features feature_importance_vals
Overall_Health__Good Overall_Health__Good 0.3125
Exercise__Yes Exercise__Yes 0.2509
Overall_Health__Excellent Overall_Health__Excellent 0.2269
ENC_timepoint_explana_is_1__2 timepoint_explana 0.2244
ENC_timepoint_explana_is_2__3 timepoint_explana 0.2233
Overall_Health__Very_good Overall_Health__Very_good 0.2144
Diabetes__No_pre_diabetes_or_borderline_diabetes Diabetes__No_pre_diabetes_or_borderline_diabetes 0.1922
Fair_or_Poor_Health__Good_or_Better_Health Fair_or_Poor_Health__Good_or_Better_Health 0.1656
Overall_Health__Fair Overall_Health__Fair 0.1579

Feature Stats

important_features feature_importance_vals unique top freq mean std min 25% 50% 75% max
Overall_Health__Good 0.3125 NA NA 0.3647059 2.9742341 -5.4 -2.375 0.45 2.900 7.7
Exercise__Yes 0.2509 NA NA 0.2264706 1.7948180 -3.2 -0.900 -0.10 1.300 7.5
Overall_Health__Excellent 0.2269 NA NA -0.3049020 4.6219746 -6.8 -4.900 -0.70 4.175 6.8
ENC_timepoint_explana_is_1__2 0.2244 2 True 51 NA NA NA NA NA NA NA
ENC_timepoint_explana_is_2__3 0.2233 2 False 51 NA NA NA NA NA NA NA
Overall_Health__Very_good 0.2144 NA NA 0.1490196 2.1523529 -7.5 -1.575 0.15 1.800 5.3
Diabetes__No_pre_diabetes_or_borderline_diabetes 0.1922 NA NA 0.2372549 0.7594841 -3.2 -0.200 0.25 0.700 3.2
Fair_or_Poor_Health__Good_or_Better_Health 0.1656 NA NA 0.2147059 3.6074290 -5.3 -3.375 0.65 3.400 6.6
Overall_Health__Fair 0.1579 NA NA 0.0568627 2.5649391 -5.4 -2.300 0.05 2.400 4.3

Input Features

input_features was_selected
Total_Population no
Alcohol_Consumption__Yes no
BMI_Categories__Normal_Weight_BMI_18_5_24_9 no
BMI_Categories__Obese_BMI_30_0_99_8 no
BMI_Categories__Overweight_BMI_25_0_29_9 no
Binge_Drinking__Yes no
Current_Smoker_Status__Yes no
Diabetes__No_pre_diabetes_or_borderline_diabetes yes
Diabetes__Yes no
Exercise__Yes yes
Fair_or_Poor_Health__Good_or_Better_Health yes
Overall_Health__Excellent yes
Overall_Health__Fair yes
Overall_Health__Good yes
Overall_Health__Poor no
Overall_Health__Very_good yes
Smoker_Status__Former_smoker no
Smoker_Status__Never_smoked no
Smoker_Status__Smoke_everyday no
Smoker_Status__Smoke_some_days no
ENC_timepoint_explana_is_1__2 yes
ENC_timepoint_explana_is_2__3 yes

Log


Binary encoded columns created for categorical input variables:

 ->  timepoint_explana: ['ENC_timepoint_explana_is_1__2', 'ENC_timepoint_explana_is_2__3']

BorutaSHAP Figures

⇨ Open PDF in new window

Pairwise Delta Dataset

⇨ Open File Directory

Model Summary

Data Pairwise
Model Type (Pass/Fail) MERF PASS; Boruta PASS
% Variance Explained 24.9% (24.6%)
N Trees 1000
Feature fraction/split 0.2
Max Depth 7
MERF Iters. 20
BorutaSHAP Trials 100
BorutaSHAP Threshold 97
P-value 0.05
N Study IDs 51
N Samples 153
Input Features 23
Accepted Features 7
Tentative Features 2
Rejected Features 14

SHAP summary beeswarm plots of feature influence on the machine learning prediction of response values. Each point represents one sample, and the horizontal position indicates impact on the response as indicated on the x-axis. Points to the left indicate a negative impact, and points to the right indicate a positive impact. The colors represent the selected feature values, where red is larger and blue is smaller. For binary encoded features (‘ENC’) red is yes[1] and blue is no[0]. SHAP is generally an improvement upon other importance scores because it provides information about both rank (how helpful the feature was compared to other features [y-axis]) and impact (a positive or negative impact on response values [x-axis]). If multiple figures are shown, scales may vary with a maximum of ten features per plot.

Selected Features

important_features decoded_features feature_importance_vals
ENC_timepoint_explana_is_1__2 timepoint_explana 0.2752
Overall_Health__Good Overall_Health__Good 0.2085
Overall_Health__Excellent Overall_Health__Excellent 0.1909
BMI_Categories__Overweight_BMI_25_0_29_9 BMI_Categories__Overweight_BMI_25_0_29_9 0.1758
Fair_or_Poor_Health__Good_or_Better_Health Fair_or_Poor_Health__Good_or_Better_Health 0.1731
Smoker_Status__Smoke_everyday Smoker_Status__Smoke_everyday 0.1726
Overall_Health__Poor Overall_Health__Poor 0.1168

Feature Stats

important_features feature_importance_vals unique top freq mean std min 25% 50% 75% max
ENC_timepoint_explana_is_1__2 0.2752 2 False 102 NA NA NA NA NA NA NA
Overall_Health__Good 0.2085 NA NA 0.4862745 2.5497435 -5.4 -1.5 0.5 2.5 7.7
Overall_Health__Excellent 0.1909 NA NA -0.4065359 3.8222973 -6.8 -3.7 -0.6 3.4 6.8
BMI_Categories__Overweight_BMI_25_0_29_9 0.1758 NA NA -0.4052288 1.4105239 -4.4 -1.4 -0.4 0.4 3.6
Fair_or_Poor_Health__Good_or_Better_Health 0.1731 NA NA 0.2862745 3.0131086 -5.3 -2.8 0.4 2.9 6.6
Smoker_Status__Smoke_everyday 0.1726 NA NA -1.3228758 0.9861873 -4.4 -2.0 -1.2 -0.5 1.0
Overall_Health__Poor 0.1168 NA NA -0.3503268 1.1068640 -3.5 -1.1 -0.4 0.5 2.4

Input Features

input_features was_selected
Total_Population no
Alcohol_Consumption__Yes no
BMI_Categories__Normal_Weight_BMI_18_5_24_9 no
BMI_Categories__Obese_BMI_30_0_99_8 no
BMI_Categories__Overweight_BMI_25_0_29_9 yes
Binge_Drinking__Yes no
Current_Smoker_Status__Yes no
Diabetes__No_pre_diabetes_or_borderline_diabetes no
Diabetes__Yes no
Exercise__Yes no
Fair_or_Poor_Health__Good_or_Better_Health yes
Overall_Health__Excellent yes
Overall_Health__Fair no
Overall_Health__Good yes
Overall_Health__Poor yes
Overall_Health__Very_good no
Smoker_Status__Former_smoker no
Smoker_Status__Never_smoked no
Smoker_Status__Smoke_everyday yes
Smoker_Status__Smoke_some_days no
ENC_timepoint_explana_is_1__2 yes
ENC_timepoint_explana_is_1__3 no
ENC_timepoint_explana_is_2__3 no

Log


Binary encoded columns created for categorical input variables:

 ->  timepoint_explana: ['ENC_timepoint_explana_is_1__2', 'ENC_timepoint_explana_is_1__3', 'ENC_timepoint_explana_is_2__3']

BorutaSHAP Figures

⇨ Open PDF in new window

See the Github repository for more information.