Use these indicators as the clearest decision-support results.
No indicators in this group for the current view.
Locality Filter All Mindanao records · 4500 of 4500 records
Review Setup
Select the service, stage, indicator, and predictor depth before reading the report.
Indicator-Level Classification Review
Classification is generated separately for each service indicator. This summary therefore focuses on
whether each indicator has enough evidence for interpretation, instead of presenting average scores
that may hide stronger and weaker indicator-level results.
The current dataset does not include direct access-barrier variables such as distance, waiting time, facility supply, staff interaction, or household service need. The portal should therefore improve the existing evidence first instead of inventing unavailable predictors.
Stage-specific modeling is applied through the saved stage response files and skip-pattern handling: Awareness uses valid awareness responses; Availment uses the availment stage; Satisfaction and Need for Action are interpreted only for respondents who reached the service-use stage.
The decision-tree pipeline now enriches the general profile and citizen-segment predictors with available service-context evidence when applicable: health background variables for Health Services, education background variables for Support to Education, and crime, disaster, corruption, and citizen-attitude evidence for Governance and Response. Feature selection remains part of the model search, while chi-square is kept in the citizen segment evidence area as profile-cluster support.
Use these as exploratory patterns and compare them with descriptive evidence.
- I1_IEMv - Clean-up Programs/Projects
Do not overstate these indicators; they need more signal before stronger prediction claims.
- I1_IEMiii - Solid Waste Management
- I1_IEMi - Community-based greening projects
- I1_IEMiv - Waste Water Management
- I1_IEMii - Air Pollution Control Program
Recommended model comparison: keep Decision Tree as the official explanation model, then add Random Forest as a performance benchmark and Logistic Regression as a simple baseline. If another model improves ROC AUC or recall, report it as supporting evidence while keeping the decision tree for readable rules.
Model Insight
For Environmental Management - Awareness, the summary combines 5 indicator-specific decision-tree model(s). Mean F1 is 69.1% and mean ROC AUC is 53.9%, so the result should be read as an overall tendency across indicators. The indicator-level rows remain the main evidence for decision-support use.
Use the mean scores for the general story; use the indicator rows for the actual evidence.
Stage eligibility is handled before modeling: Awareness uses all valid Yes/No responses; Availment uses Awareness = Yes; Satisfaction and Need for Action use Awareness = Yes and Availment = Yes. Skip-pattern values such as 95-99 are excluded from the target class.
- Mean precision is 74.0%, indicating moderate when an indicator model predicts the positive service outcome.
- Mean recall is 65.5%, meaning some indicator models may still miss actual positive cases, especially when recall is lower than precision.
- Mean F1 score is 69.1%, which balances precision and recall across the indicator models.
- Mean ROC AUC is 53.9%, showing how well the indicator models separate outcome groups across thresholds on average.
- Top predictors currently include A5, B5, MCA DimMagnitude, MCA Dim2, B4. These variables are useful signals for explaining which respondent profiles are linked to the selected service outcome.
- Interpretation: the model has limited separation power. Use the predictors as exploratory clues and validate findings with descriptive charts and local service context.
Top Predictors
These are the strongest variables used by the saved decision-tree outputs for the selected service area and stage.
-
Community-based greening projects
A5 -
Community-based greening projects
B5 -
Community-based greening projects
MCA DimMagnitude -
Air Pollution Control Program
MCA Dim2 -
Air Pollution Control Program
MCA DimMagnitude -
Air Pollution Control Program
B4 -
Solid Waste Management
A5 -
Solid Waste Management
MCA DimMagnitude
Predictor Importance Chart
Show the ranked feature importance chart for the selected service-stage model.
Optional detail
Predictor Importance Chart
Show the ranked feature importance chart for the selected service-stage model.
Top Predictors
Recurring Predictors Across Decision Tree Models
This review counts which predictors repeatedly appear with positive importance across the saved indicator-level Decision Tree models. It helps identify citizen characteristics that consistently support interpretation across service-stage outcomes.
A recurring predictor is decision-support evidence, not proof of causation. Use it together with the indicator-level metrics, decision-tree example, clustering results, and service delivery review.
| Predictor | Predictor Group | Appeared in Models | Service Areas | Service Stages | Mean Importance | Plain Interpretation |
|---|---|---|---|---|---|---|
| MCA Dim2 | MCA profile dimension | 124 | 7 | 4 | 0.2751 | This is a combined profile signal from MCA. It should be read as a summary of related respondent characteristics, not as one survey question. |
| MCA Dim1 | MCA profile dimension | 113 | 7 | 4 | 0.2629 | This is a combined profile signal from MCA. It should be read as a summary of related respondent characteristics, not as one survey question. |
| MCA DimMagnitude | MCA profile dimension | 101 | 7 | 4 | 0.2349 | This is a combined profile signal from MCA. It should be read as a summary of related respondent characteristics, not as one survey question. |
| B4 | Housing and living conditions | 92 | 7 | 4 | 0.1659 | Household conditions or information access may be linked with how citizens know about and use LGU services. |
| B6 | Housing and living conditions | 92 | 7 | 4 | 0.155 | Household conditions or information access may be linked with how citizens know about and use LGU services. |
| A5 | Socio-demographic profile | 87 | 7 | 4 | 0.1811 | This respondent profile characteristic may help explain differences in awareness, service use, satisfaction, or need for action. |
| A3.1 | Socio-demographic profile | 75 | 7 | 4 | 0.151 | This respondent profile characteristic may help explain differences in awareness, service use, satisfaction, or need for action. |
| A1 | Socio-demographic profile | 59 | 7 | 4 | 0.126 | This respondent profile characteristic may help explain differences in awareness, service use, satisfaction, or need for action. |
| A7 | Socio-demographic profile | 56 | 7 | 4 | 0.1261 | This respondent profile characteristic may help explain differences in awareness, service use, satisfaction, or need for action. |
| B2 | Housing and living conditions | 53 | 7 | 4 | 0.1056 | Household conditions or information access may be linked with how citizens know about and use LGU services. |
| B1 | Housing and living conditions | 50 | 7 | 4 | 0.1258 | Household conditions or information access may be linked with how citizens know about and use LGU services. |
| B3 | Housing and living conditions | 44 | 7 | 4 | 0.127 | Household conditions or information access may be linked with how citizens know about and use LGU services. |
| A4 | Socio-demographic profile | 42 | 7 | 4 | 0.1198 | This respondent profile characteristic may help explain differences in awareness, service use, satisfaction, or need for action. |
| A2 | Socio-demographic profile | 30 | 7 | 4 | 0.0739 | This respondent profile characteristic may help explain differences in awareness, service use, satisfaction, or need for action. |
| A8 | Socio-demographic profile | 28 | 6 | 4 | 0.0957 | This respondent profile characteristic may help explain differences in awareness, service use, satisfaction, or need for action. |
Decision Tree Interpretation
Use this view to understand the main decision logic before opening the technical tree diagram.
How to read this result
Interpretation: among respondents with A7 <= 2.500, the model tends to predict awareness.
Some respondents with A7 > 2.500, especially those outside Cluster 0 are predicted Not Aware.
Key technical terms
- First split The first condition is the strongest separator used by the simplified tree.
- Samples The number of eligible respondent records that reached a node.
- Value The distribution of respondents across the two possible outcome classes.
- Class The predicted outcome for that branch, such as aware or not aware.
Predictors shown in the simplified tree
A5
B5
MCACluster
A7
Technical Tree Diagram Optional detail
Evidence Tables
Show readiness review, indicator metrics, rule reference, and diagnostics.
Optional detail
Evidence Tables
Show readiness review, indicator metrics, rule reference, and diagnostics.
Model Readiness Review
This table explains whether each indicator has enough signal for decision-support use and why some results should remain exploratory.
| Indicator | Signal Quality (F1 / ROC AUC / Recall / Baseline) | Meaning | Majority Response (Baseline Class) | Eligible Records | Baseline Accuracy (Majority Guess) | Model Accuracy | Gain (Model - Baseline) | Possible Cause | Suggested Data Improvement |
|---|---|---|---|---|---|---|---|---|---|
| I1_IEMv Clean-up Programs/Projects |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (82.6%) | 4350 | 82.6% | 70.1% | -12.4 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Environmental Management. |
| I1_IEMiii Solid Waste Management |
Limited signal | The model can be reviewed as an exploratory clue, but F1, ROC AUC, or baseline gain is not strong enough for confident prediction. | Yes / Positive (86.9%) | 4350 | 86.9% | 69.3% | -17.6 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Environmental Management. |
| I1_IEMi Community-based greening projects |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | Yes / Positive (76.8%) | 4350 | 76.8% | 57.9% | -18.9 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Environmental Management. |
| I1_IEMiv Waste Water Management |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | No / Negative (50.3%) | 3150 | 50.3% | 54.5% | +4.3 pts | Citizen profiles may be too similar across outcome groups, or the survey may lack service-specific predictors. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Environmental Management. |
| I1_IEMii Air Pollution Control Program |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | Yes / Positive (66.1%) | 3600 | 66.1% | 49.2% | -16.9 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Environmental Management. |
Indicator-Level Metrics
How the mean scores are formed
The AVG values summarize 5 indicator model(s) for Environmental Management - Awareness. They describe the overall pattern across indicators, not the result of one specific indicator.
Average F1 is 69.1%, which gives the most balanced quick reading because it considers both correct positive predictions and missed positive cases.
Average ROC AUC is 53.9%, so the model should be read as decision-support evidence. Values closer to 50% mean the predictors have limited ability to separate the outcome groups.
Average accuracy is 60.2%, precision is 74.0%, and recall is 65.5%. Compare the indicator rows below to see which indicators are stronger or weaker than the AVG.
| Target | Indicator | Accuracy | Precision | Recall | F1 | ROC AUC | CV F1 | Best CV F1 | Selected Features | Base Predictors | Service Context | Total Predictors | Depth | Leaf | Criterion |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| I1_IEMv | Clean-up Programs/Projects | 70.1% | 85.8% | 76.8% | 81.1% | 59.9% | 72.2% | 75.3% | 16 | 17 | 0 | 17 | 3 | 10 | gini |
| I1_IEMiii | Solid Waste Management | 69.3% | 88.6% | 74.6% | 81.0% | 54.8% | 76.7% | 80.2% | 8 | 17 | 0 | 17 | 3 | 10 | entropy |
| I1_IEMi | Community-based greening projects | 57.9% | 76.0% | 65.5% | 70.4% | 49.6% | 58.3% | 69.6% | 8 | 17 | 0 | 17 | 3 | 10 | gini |
| I1_IEMiv | Waste Water Management | 54.5% | 54.0% | 63.1% | 58.2% | 54.7% | 55.1% | 59.7% | 16 | 17 | 0 | 17 | 5 | 10 | gini |
| I1_IEMii | Air Pollution Control Program | 49.2% | 65.5% | 47.3% | 54.9% | 50.2% | 60.7% | 65.1% | all | 17 | 0 | 17 | 5 | 10 | gini |
Decision Rule Reference
Some tree rules use coded profile values. Read the tree from top to bottom, then use this reference to translate the rule into respondent-friendly meaning.
| Code | Meaning | Code Values / Variable Type | How to Read the Split |
|---|---|---|---|
| A7 | Employment Status | 1=Working at least 40 hrs/wk; 2=Working less than 40 hrs/wk; 3=Not employed but looking for work; have worked in the past; 4=Not employed but looking for work; have not worked in the past; 5=No job, not looking for work; have not worked in the past; 6=Not employed, not looking for work; have worked in the past; 7=Student (not working); 8=Retired (not working) / Too old to work | <= 2.5 means Working at least 40 hrs/wk, Working less than 40 hrs/wk; > 2.5 means Not employed but looking for work; have worked in the past, Not employed but looking for work; have not worked in the past, No job, not looking for work; have not worked in the past, Not employed, not looking for work; have worked in the past, Student (not working), Retired (not working) / Too old to work. |
| B5 | Source of Electricty | 1=ELECTRICITY OWN Connection; 2=Electricity Shared Connection; 3=BATTERY; 4=GENERATOR; 5=NONE; 99=Others | <= 1.5 means ELECTRICITY OWN Connection; > 1.5 means Electricity Shared Connection, BATTERY, GENERATOR, NONE, Others. |
| A5 | Highest Educational Attainment | 1=Elem Undergraduate; 2=Elem Graduate; 3= Hi-Sch Undergraduate; 4=Hi Sch Graduate; 5=College Undergrad; 6=College Graduate ; 7= Masters Undergrad; 8=Masters Graduate; 9=Doctorate; 10=Vocational /TVET; 11=Apprenticeship; 99=Others | <= 7.5 means Elem Undergraduate, Elem Graduate, Hi-Sch Undergraduate, Hi Sch Graduate, College Undergrad, College Graduate , Masters Undergrad; > 7.5 means Masters Graduate, Doctorate, Vocational /TVET, Apprenticeship, Others. |
| MCACluster_0 | Citizen segment | Cluster membership from the citizen segmentation model. | Use this as a respondent segment indicator, not as a direct survey answer. |
Gini Split Diagnostics
| Node | Type | Rule | Gini | Samples | Not Aware | Aware | Prediction |
|---|---|---|---|---|---|---|---|
| 0 | Split | A7 <= 2.500 | 0.5 | 4350 | 0.5 | 0.5 | Aware |
| 1 | Split | B5 <= 1.500 | 0.4975 | 1934 | 0.5 | 0.5 | Aware |
| 2 | Split | A5 <= 7.500 | 0.4948 | 1640 | 0.4 | 0.6 | Aware |
| 3 | Leaf | Prediction | 0.4958 | 1553 | 0.5 | 0.5 | Aware |
| 4 | Leaf | Prediction | 0.4526 | 87 | 0.3 | 0.7 | Aware |
| 5 | Split | B5 <= 3.500 | 0.4961 | 294 | 0.5 | 0.5 | Not Aware |
| 6 | Leaf | Prediction | 0.4994 | 249 | 0.5 | 0.5 | Not Aware |
| 7 | Leaf | Prediction | 0.4439 | 45 | 0.7 | 0.3 | Not Aware |
| 8 | Split | A5 <= 4.500 | 0.4986 | 2416 | 0.5 | 0.5 | Not Aware |
| 9 | Split | MCACluster_0 <= 0.500 | 0.4963 | 1672 | 0.5 | 0.5 | Not Aware |
| 10 | Leaf | Prediction | 0.4974 | 1546 | 0.5 | 0.5 | Not Aware |
| 11 | Leaf | Prediction | 0.4697 | 126 | 0.6 | 0.4 | Not Aware |
| 12 | Split | A5 <= 8.500 | 0.4997 | 744 | 0.5 | 0.5 | Aware |
| 13 | Leaf | Prediction | 0.5 | 652 | 0.5 | 0.5 | Aware |
| 14 | Leaf | Prediction | 0.4766 | 92 | 0.4 | 0.6 | Aware |