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.
- G1_GGPv - Timely Response on Peace and Order andPublic Safety-related incidents
- G1_GGPiv - Conflict and dispute resolution in the barangays
- G1_GGPi - Delivery of Frontline services
- G1_GGPvii - Disaster Risk Reduction and Management
Do not overstate these indicators; they need more signal before stronger prediction claims.
- G1_GGPiii - Mobile LGUservices/Provision ofmunicipal services to the barangays
- G1_GGPii - Local government’s response or action oncomplaints against an office,official orpersonnel of the LGU
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 Governance and Response - Awareness, the summary combines 8 indicator-specific decision-tree model(s). Mean F1 is 74.4% and mean ROC AUC is 57.9%, 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 77.9%, indicating strong when an indicator model predicts the positive service outcome.
- Mean recall is 73.4%, meaning some indicator models may still miss actual positive cases, especially when recall is lower than precision.
- Mean F1 score is 74.4%, which balances precision and recall across the indicator models.
- Mean ROC AUC is 57.9%, showing how well the indicator models separate outcome groups across thresholds on average.
- Top predictors currently include G6, MCA Dim1, MCA Dim2, B6, A4. These variables are useful signals for explaining which respondent profiles are linked to the selected service outcome.
- The current classification pipeline also includes up to 18 service-context predictor(s) for this view, so the model is no longer limited to general profile and cluster variables.
- 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.
-
Delivery of Frontline services
G6 -
Delivery of Frontline services
MCA Dim1 -
Delivery of Frontline services
MCA Dim2 -
Local government’s response or action oncomplaints against an office,official orpersonnel of the LGU
MCA Dim2 -
Local government’s response or action oncomplaints against an office,official orpersonnel of the LGU
B6 -
Local government’s response or action oncomplaints against an office,official orpersonnel of the LGU
A4 -
Mobile LGUservices/Provision ofmunicipal services to the barangays
G6 -
Mobile LGUservices/Provision ofmunicipal services to the barangays
Cluster 2
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 MCA Dim2 <= -0.413, especially those in the associated MCA profile range, the model tends to predict awareness.
Some respondents with MCA Dim2 > -0.413, especially those in the associated MCA profile range, especially those in the associated MCA profile range 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
MCA Dim1
MCA Dim2
B3
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 |
|---|---|---|---|---|---|---|---|---|---|
| G1_GGPv Timely Response on Peace and Order andPublic Safety-related incidents |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (87.7%) | 4350 | 87.7% | 82.2% | -5.5 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 Governance and Response. |
| G1_GGPiv Conflict and dispute resolution in the barangays |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (83.8%) | 4200 | 83.8% | 77.9% | -5.9 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 Governance and Response. |
| G1_GGPi Delivery of Frontline services |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (84.0%) | 4500 | 84.0% | 74.5% | -9.4 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 Governance and Response. |
| G1_GGPvii Disaster Risk Reduction and Management |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (76.3%) | 4350 | 76.3% | 74.9% | -1.4 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 Governance and Response. |
| G1_GGPviii Public Information Services |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (70.3%) | 4200 | 70.3% | 66.0% | -4.3 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 Governance and Response. |
| G1_GGPvi Traffic Management |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (61.6%) | 4050 | 61.6% | 63.8% | +2.2 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 Governance and Response. |
| G1_GGPiii Mobile LGUservices/Provision ofmunicipal services to the barangays |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | Yes / Positive (69.1%) | 4050 | 69.1% | 50.7% | -18.4 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 Governance and Response. |
| G1_GGPii Local government’s response or action oncomplaints against an office,official orpersonnel of the LGU |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | Yes / Positive (63.8%) | 4050 | 63.8% | 45.0% | -18.8 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 Governance and Response. |
Indicator-Level Metrics
How the mean scores are formed
The AVG values summarize 8 indicator model(s) for Governance and Response - Awareness. They describe the overall pattern across indicators, not the result of one specific indicator.
Average F1 is 74.4%, which gives the most balanced quick reading because it considers both correct positive predictions and missed positive cases.
Average ROC AUC is 57.9%, 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 66.9%, precision is 77.9%, and recall is 73.4%. 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| G1_GGPv | Timely Response on Peace and Order andPublic Safety-related incidents | 82.2% | 91.1% | 88.5% | 89.8% | 63.5% | 90.0% | 90.7% | 16 | 17 | 18 | 35 | 3 | 10 | entropy |
| G1_GGPiv | Conflict and dispute resolution in the barangays | 77.9% | 84.3% | 90.1% | 87.1% | 55.1% | 76.6% | 86.5% | 8 | 17 | 18 | 35 | 3 | 10 | entropy |
| G1_GGPi | Delivery of Frontline services | 74.5% | 86.2% | 83.2% | 84.7% | 57.6% | 85.8% | 85.3% | 16 | 17 | 18 | 35 | 5 | 10 | entropy |
| G1_GGPvii | Disaster Risk Reduction and Management | 74.9% | 79.0% | 91.2% | 84.7% | 58.4% | 70.0% | 76.5% | 8 | 17 | 18 | 35 | 3 | 10 | gini |
| G1_GGPviii | Public Information Services | 66.0% | 76.5% | 73.8% | 75.1% | 64.3% | 73.6% | 78.1% | 8 | 17 | 18 | 35 | 6 | 10 | entropy |
| G1_GGPvi | Traffic Management | 63.8% | 67.9% | 82.0% | 74.3% | 59.2% | 68.0% | 72.1% | 16 | 17 | 18 | 35 | 3 | 10 | gini |
| G1_GGPiii | Mobile LGUservices/Provision ofmunicipal services to the barangays | 50.7% | 75.3% | 46.4% | 57.4% | 55.3% | 57.7% | 76.4% | 8 | 17 | 18 | 35 | 5 | 10 | entropy |
| G1_GGPii | Local government’s response or action oncomplaints against an office,official orpersonnel of the LGU | 45.0% | 63.2% | 31.5% | 42.1% | 49.9% | 66.6% | 65.7% | 16 | 17 | 18 | 35 | 5 | 10 | entropy |
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 |
|---|---|---|---|
| Dim2 | MCA profile dimension 2 | A combined respondent-profile score from MCA. It is not a single survey question. | Dim2 <= -0.413 follows one side of the profile map; values above -0.413 follow the other side. |
| B3 | HH Toilet Type | 1=Flush/Water-Sealed: OWN TOILET; 2=Flush/Water-Sealed: Shared; 3=PIT TOILET/LATRINE; 4=DROP/OVERHANG; 5=NO TOILET/OPEN FIELD; 99=OTHERS | <= 3.5 means Flush/Water-Sealed: OWN TOILET, Flush/Water-Sealed: Shared, PIT TOILET/LATRINE; > 3.5 means DROP/OVERHANG, NO TOILET/OPEN FIELD, OTHERS. |
Gini Split Diagnostics
| Node | Type | Rule | Gini | Samples | Not Aware | Aware | Prediction |
|---|---|---|---|---|---|---|---|
| 0 | Split | Dim2 <= -0.413 | 0.5 | 4500 | 0.5 | 0.5 | Aware |
| 1 | Split | B3 <= 3.500 | 0.4938 | 1698 | 0.4 | 0.6 | Aware |
| 2 | Split | Dim2 <= -0.577 | 0.4917 | 1664 | 0.4 | 0.6 | Aware |
| 3 | Leaf | Prediction | 0.4944 | 1508 | 0.4 | 0.6 | Aware |
| 4 | Leaf | Prediction | 0.4229 | 156 | 0.3 | 0.7 | Aware |
| 5 | Leaf | Prediction | 0.408 | 34 | 0.7 | 0.3 | Not Aware |
| 6 | Split | Dim2 <= 0.700 | 0.4982 | 2802 | 0.5 | 0.5 | Not Aware |
| 7 | Split | Dim2 <= 0.658 | 0.491 | 1389 | 0.6 | 0.4 | Not Aware |
| 8 | Leaf | Prediction | 0.4935 | 1327 | 0.6 | 0.4 | Not Aware |
| 9 | Leaf | Prediction | 0.3958 | 62 | 0.7 | 0.3 | Not Aware |
| 10 | Split | Dim2 <= 1.628 | 0.4998 | 1413 | 0.5 | 0.5 | Aware |
| 11 | Leaf | Prediction | 0.4959 | 1216 | 0.5 | 0.5 | Aware |
| 12 | Leaf | Prediction | 0.4536 | 197 | 0.7 | 0.3 | Not Aware |