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.
- J1_JEAiv - Distribution of planting/farming/fishing materials and/or equipment
- J1_JETiii - Investment promotion activities such as trade fairs, fiestas, business events and similar events
Do not overstate these indicators; they need more signal before stronger prediction claims.
- J1_JEEiv - Livelihood Programs
- J1_JEAi - Organization and development of farmers, fishermen and their cooperatives
- J1_JEAii - Access to irrigation facilities or equipment
- J1_JEEi - Public Employment Services
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 Economic and Investment Promotion - Awareness, the summary combines 16 indicator-specific decision-tree model(s). Mean F1 is 53.2% and mean ROC AUC is 54.0%, 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 53.1%, indicating limited to moderate when an indicator model predicts the positive service outcome.
- Mean recall is 55.9%, meaning some indicator models may still miss actual positive cases, especially when recall is lower than precision.
- Mean F1 score is 53.2%, which balances precision and recall across the indicator models.
- Mean ROC AUC is 54.0%, showing how well the indicator models separate outcome groups across thresholds on average.
- Top predictors currently include MCA Dim1, MCA Dim2, B3, MCA DimMagnitude, A3.1. 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.
-
Organization and development of farmers, fishermen and their cooperatives
MCA Dim1 -
Organization and development of farmers, fishermen and their cooperatives
MCA Dim2 -
Organization and development of farmers, fishermen and their cooperatives
B3 -
Access to irrigation facilities or equipment
MCA DimMagnitude -
Access to irrigation facilities or equipment
B3 -
Access to irrigation facilities or equipment
A3.1 -
Prevention and control of plant and animal pests and diseases; fish kill sand diseases
A5 -
Prevention and control of plant and animal pests and diseases; fish kill sand diseases
MCA Dim2
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 A5 <= 4.500, especially those in the associated MCA profile range, the model tends to predict lack of awareness.
Some respondents with A5 > 4.500, especially those in the associated MCA profile range are predicted 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
A5
B6
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 |
|---|---|---|---|---|---|---|---|---|---|
| J1_JEEiv Livelihood 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 (65.8%) | 4200 | 65.8% | 61.2% | -4.6 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 Economic and Investment Promotion. |
| J1_JEAi Organization and development of farmers, fishermen and their cooperatives |
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 (65.2%) | 4200 | 65.2% | 57.7% | -7.5 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 Economic and Investment Promotion. |
| J1_JEAii Access to irrigation facilities or equipment |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | Yes / Positive (54.1%) | 3600 | 54.1% | 55.0% | +0.9 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 Economic and Investment Promotion. |
| J1_JEAiv Distribution of planting/farming/fishing materials and/or equipment |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (63.6%) | 4200 | 63.6% | 56.4% | -7.2 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 Economic and Investment Promotion. |
| J1_JETiii Investment promotion activities such as trade fairs, fiestas, business events and similar events |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (54.1%) | 4050 | 54.1% | 55.9% | +1.8 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 Economic and Investment Promotion. |
| J1_JEEi Public Employment Services |
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 (57.2%) | 4050 | 57.2% | 53.8% | -3.4 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 Economic and Investment Promotion. |
| J1_JEEii Regulation and supervision of businesses |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | Yes / Positive (59.7%) | 4050 | 59.7% | 55.5% | -4.2 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 Economic and Investment Promotion. |
| J1_JETi Development and maintenance of touristattractions and facilities |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | No / Negative (58.6%) | 4200 | 58.6% | 56.0% | -2.7 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 Economic and Investment Promotion. |
| J1_JEAiii Prevention and control of plant and animal pests and diseases; fish kill sand diseases |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | Yes / Positive (59.6%) | 4200 | 59.6% | 51.0% | -8.6 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 Economic and Investment Promotion. |
| J1_JEAviii Accessible farmharvest buying/trading stations |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | No / Negative (50.6%) | 3300 | 50.6% | 53.6% | +3.0 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 Economic and Investment Promotion. |
| J1_JEAvii Post-Harvest facilities such as crop dryers, slaughter houses or fish processing facilities |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | No / Negative (51.4%) | 4200 | 51.4% | 53.7% | +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 Economic and Investment Promotion. |
| J1_JETii Product/Brandmarketing and promotion of local goods and touristattractions |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | No / Negative (68.7%) | 3750 | 68.7% | 53.4% | -15.2 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 Economic and Investment Promotion. |
| J1_JEAvi Water and soilresource utilization and conservation projects |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | No / Negative (63.9%) | 3600 | 63.9% | 50.4% | -13.5 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 Economic and Investment Promotion. |
| J1_JETiv Organization,accreditation and training of tourism related concessions. |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | No / Negative (76.7%) | 2550 | 76.7% | 49.9% | -26.8 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 Economic and Investment Promotion. |
| J1_JEEiii Promotion of Barangay Micro Business Enterprises |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | No / Negative (60.5%) | 3150 | 60.5% | 53.6% | -6.9 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 Economic and Investment Promotion. |
| J1_JEAv Access to facilities that promote agricultural production such as fish hatcheries and breeding stations |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | No / Negative (67.8%) | 3600 | 67.8% | 62.8% | -5.0 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 Economic and Investment Promotion. |
Indicator-Level Metrics
How the mean scores are formed
The AVG values summarize 16 indicator model(s) for Economic and Investment Promotion - Awareness. They describe the overall pattern across indicators, not the result of one specific indicator.
Average F1 is 53.2%, which gives the most balanced quick reading because it considers both correct positive predictions and missed positive cases.
Average ROC AUC is 54.0%, 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 55.0%, precision is 53.1%, and recall is 55.9%. 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| J1_JEEiv | Livelihood Programs | 61.2% | 70.3% | 71.6% | 70.9% | 54.2% | 64.7% | 68.4% | 16 | 17 | 0 | 17 | 5 | 10 | entropy |
| J1_JEAi | Organization and development of farmers, fishermen and their cooperatives | 57.7% | 65.9% | 72.4% | 69.0% | 53.0% | 62.0% | 69.0% | 8 | 17 | 0 | 17 | 6 | 10 | gini |
| J1_JEAii | Access to irrigation facilities or equipment | 55.0% | 57.8% | 78.6% | 66.6% | 50.1% | 62.7% | 65.0% | 8 | 17 | 0 | 17 | 3 | 10 | entropy |
| J1_JEAiv | Distribution of planting/farming/fishing materials and/or equipment | 56.4% | 68.7% | 59.6% | 63.8% | 55.6% | 65.3% | 62.9% | 8 | 17 | 0 | 17 | 5 | 10 | entropy |
| J1_JETiii | Investment promotion activities such as trade fairs, fiestas, business events and similar events | 55.9% | 58.6% | 65.1% | 61.7% | 55.8% | 59.2% | 60.4% | 8 | 17 | 0 | 17 | 3 | 35 | gini |
| J1_JEEi | Public Employment Services | 53.8% | 60.5% | 60.1% | 60.3% | 53.2% | 59.2% | 63.6% | 16 | 17 | 0 | 17 | 6 | 10 | entropy |
| J1_JEEii | Regulation and supervision of businesses | 55.5% | 67.5% | 53.5% | 59.7% | 56.7% | 62.2% | 63.8% | all | 17 | 0 | 17 | 6 | 35 | gini |
| J1_JETi | Development and maintenance of touristattractions and facilities | 56.0% | 47.9% | 65.5% | 55.3% | 59.0% | 54.2% | 57.5% | 16 | 17 | 0 | 17 | 3 | 35 | gini |
| J1_JEAiii | Prevention and control of plant and animal pests and diseases; fish kill sand diseases | 51.0% | 63.8% | 45.0% | 52.7% | 52.2% | 50.7% | 68.5% | 8 | 17 | 0 | 17 | 3 | 35 | entropy |
| J1_JEAviii | Accessible farmharvest buying/trading stations | 53.6% | 55.0% | 50.0% | 52.4% | 54.2% | 53.0% | 60.5% | 16 | 17 | 0 | 17 | 3 | 35 | gini |
| J1_JEAvii | Post-Harvest facilities such as crop dryers, slaughter houses or fish processing facilities | 53.7% | 54.8% | 46.0% | 50.0% | 53.3% | 52.7% | 56.1% | 8 | 17 | 0 | 17 | 5 | 35 | gini |
| J1_JETii | Product/Brandmarketing and promotion of local goods and touristattractions | 53.4% | 35.4% | 52.1% | 42.2% | 53.1% | 45.3% | 46.7% | 16 | 17 | 0 | 17 | 3 | 35 | entropy |
| J1_JEAvi | Water and soilresource utilization and conservation projects | 50.4% | 37.8% | 46.5% | 41.7% | 51.5% | 46.2% | 49.2% | 8 | 17 | 0 | 17 | 5 | 10 | gini |
| J1_JETiv | Organization,accreditation and training of tourism related concessions. | 49.9% | 28.6% | 73.9% | 41.2% | 60.2% | 37.8% | 39.3% | 16 | 17 | 0 | 17 | 3 | 10 | gini |
| J1_JEEiii | Promotion of Barangay Micro Business Enterprises | 53.6% | 42.2% | 37.7% | 39.8% | 53.1% | 49.5% | 52.3% | all | 17 | 0 | 17 | 5 | 10 | gini |
| J1_JEAv | Access to facilities that promote agricultural production such as fish hatcheries and breeding stations | 62.8% | 35.0% | 17.3% | 23.1% | 49.4% | 42.6% | 45.9% | 16 | 17 | 0 | 17 | 3 | 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 |
|---|---|---|---|
| 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 | <= 4.5 means Elem Undergraduate, Elem Graduate, Hi-Sch Undergraduate, Hi Sch Graduate; > 4.5 means College Undergrad, College Graduate , Masters Undergrad, Masters Graduate, Doctorate, Vocational /TVET, Apprenticeship, Others. |
| Dim1 | MCA profile dimension 1 | A combined respondent-profile score from MCA. It is not a single survey question. | Dim1 <= -0.156 follows one side of the profile map; values above -0.156 follow the other side. |
| Dim2 | MCA profile dimension 2 | A combined respondent-profile score from MCA. It is not a single survey question. | Dim2 <= -1.22 follows one side of the profile map; values above -1.22 follow the other side. |
Gini Split Diagnostics
| Node | Type | Rule | Gini | Samples | Not Aware | Aware | Prediction |
|---|---|---|---|---|---|---|---|
| 0 | Split | A5 <= 4.500 | 0.5 | 4050 | 0.5 | 0.5 | Aware |
| 1 | Split | A5 <= 2.500 | 0.4982 | 2752 | 0.5 | 0.5 | Not Aware |
| 2 | Split | Dim1 <= -0.156 | 0.4922 | 1088 | 0.6 | 0.4 | Not Aware |
| 3 | Leaf | Prediction | 0.4891 | 951 | 0.6 | 0.4 | Not Aware |
| 4 | Leaf | Prediction | 0.4992 | 137 | 0.5 | 0.5 | Aware |
| 5 | Split | Dim1 <= -0.529 | 0.4999 | 1664 | 0.5 | 0.5 | Not Aware |
| 6 | Leaf | Prediction | 0.4926 | 474 | 0.4 | 0.6 | Aware |
| 7 | Leaf | Prediction | 0.4976 | 1190 | 0.5 | 0.5 | Not Aware |
| 8 | Split | A5 <= 54.500 | 0.4915 | 1298 | 0.4 | 0.6 | Aware |
| 9 | Split | Dim2 <= -1.220 | 0.4887 | 1251 | 0.4 | 0.6 | Aware |
| 10 | Leaf | Prediction | 0.4522 | 244 | 0.3 | 0.7 | Aware |
| 11 | Leaf | Prediction | 0.4937 | 1007 | 0.4 | 0.6 | Aware |
| 12 | Leaf | Prediction | 0.4329 | 47 | 0.7 | 0.3 | Not Aware |