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.
- E6_EBSii - Sports programs and activities
- E6_EBSi - Provision of medical and/or nutritional services to school clinics
Do not overstate these indicators; they need more signal before stronger prediction claims.
- E6_EBSiii - Scholarships and other assistance programs for students
- E6_EBSiv - Alternative LearningSystem and/or otherSpecial Education Programs
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 Support to Education - Awareness, the summary combines 4 indicator-specific decision-tree model(s). Mean F1 is 73.1% and mean ROC AUC is 56.3%, 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 84.0%, indicating strong positive predictions when an indicator model predicts the positive service outcome.
- Mean recall is 66.7%, meaning some indicator models may still miss actual positive cases, especially when recall is lower than precision.
- Mean F1 score is 73.1%, which balances precision and recall across the indicator models.
- Mean ROC AUC is 56.3%, showing how well the indicator models separate outcome groups across thresholds on average.
- Top predictors currently include E3 Level (K-12) 9, MCA Dim1, MCA Dim2, E1, A3.1. 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 19 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.
-
Provision of medical and/or nutritional services to school clinics
E3 Level (K-12) 9 -
Provision of medical and/or nutritional services to school clinics
MCA Dim1 -
Provision of medical and/or nutritional services to school clinics
MCA Dim2 -
Sports programs and activities
E1 -
Sports programs and activities
A3.1 -
Sports programs and activities
Cluster 4 -
Scholarships and other assistance programs for students
MCA Dim1 -
Scholarships and other assistance programs for students
A5
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 Dim1 > -0.680, especially those in the associated MCA profile range, especially those in the associated MCA profile range, the model tends to predict awareness.
Some respondents with MCA Dim1 <= -0.680, 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
MCACluster
A6
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 |
|---|---|---|---|---|---|---|---|---|---|
| E6_EBSiii Scholarships and other assistance programs for students |
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 (77.3%) | 4350 | 77.3% | 75.2% | -2.1 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 Support to Education. |
| E6_EBSii Sports programs and activities |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (84.9%) | 4200 | 84.9% | 57.7% | -27.2 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 Support to Education. |
| E6_EBSiv Alternative LearningSystem and/or otherSpecial Education Programs |
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 (83.4%) | 4050 | 83.4% | 57.2% | -26.2 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 Support to Education. |
| E6_EBSi Provision of medical and/or nutritional services to school clinics |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (80.6%) | 4350 | 80.6% | 56.7% | -24.0 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 Support to Education. |
Indicator-Level Metrics
How the mean scores are formed
The AVG values summarize 4 indicator model(s) for Support to Education - Awareness. They describe the overall pattern across indicators, not the result of one specific indicator.
Average F1 is 73.1%, which gives the most balanced quick reading because it considers both correct positive predictions and missed positive cases.
Average ROC AUC is 56.3%, 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 61.7%, precision is 84.0%, and recall is 66.7%. 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| E6_EBSiii | Scholarships and other assistance programs for students | 75.2% | 78.6% | 93.6% | 85.4% | 53.2% | 72.6% | 75.1% | 8 | 17 | 19 | 36 | 5 | 10 | entropy |
| E6_EBSii | Sports programs and activities | 57.7% | 88.3% | 58.2% | 70.2% | 58.3% | 74.0% | 78.8% | 8 | 17 | 19 | 36 | 3 | 35 | gini |
| E6_EBSiv | Alternative LearningSystem and/or otherSpecial Education Programs | 57.2% | 86.0% | 58.1% | 69.4% | 55.0% | 70.5% | 78.7% | 16 | 17 | 19 | 36 | 6 | 10 | entropy |
| E6_EBSi | Provision of medical and/or nutritional services to school clinics | 56.7% | 83.1% | 57.0% | 67.6% | 58.6% | 65.8% | 71.4% | 8 | 17 | 19 | 36 | 3 | 35 | 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 |
|---|---|---|---|
| Dim1 | MCA profile dimension 1 | A combined respondent-profile score from MCA. It is not a single survey question. | Dim1 <= -0.68 follows one side of the profile map; values above -0.68 follow the other side. |
| Dim2 | MCA profile dimension 2 | A combined respondent-profile score from MCA. It is not a single survey question. | Dim2 <= -1.326 follows one side of the profile map; values above -1.326 follow the other side. |
Gini Split Diagnostics
| Node | Type | Rule | Gini | Samples | Not Aware | Aware | Prediction |
|---|---|---|---|---|---|---|---|
| 0 | Split | Dim1 <= -0.680 | 0.5 | 4350 | 0.5 | 0.5 | Not Aware |
| 1 | Split | Dim2 <= -1.326 | 0.4851 | 594 | 0.6 | 0.4 | Not Aware |
| 2 | Leaf | Prediction | 0.3823 | 26 | 0.3 | 0.7 | Aware |
| 3 | Split | Dim1 <= -0.691 | 0.4813 | 568 | 0.6 | 0.4 | Not Aware |
| 4 | Leaf | Prediction | 0.4867 | 527 | 0.6 | 0.4 | Not Aware |
| 5 | Leaf | Prediction | 0.3783 | 41 | 0.7 | 0.3 | Not Aware |
| 6 | Split | Dim2 <= -0.164 | 0.4995 | 3756 | 0.5 | 0.5 | Aware |
| 7 | Split | Dim1 <= 3.673 | 0.4927 | 1525 | 0.4 | 0.6 | Aware |
| 8 | Leaf | Prediction | 0.4939 | 1498 | 0.4 | 0.6 | Aware |
| 9 | Leaf | Prediction | 0.0 | 27 | 0.0 | 1.0 | Aware |
| 10 | Split | Dim1 <= -0.497 | 0.4997 | 2231 | 0.5 | 0.5 | Not Aware |
| 11 | Leaf | Prediction | 0.4846 | 376 | 0.6 | 0.4 | Not Aware |
| 12 | Leaf | Prediction | 0.5 | 1855 | 0.5 | 0.5 | Aware |