Classification Insights

Decision-tree evidence for selected CSIS service indicators.

Awareness
Locality Filter All Mindanao records · 4500 of 4500 records
Locality Filter All Mindanao records

Showing 4500 of 4500 respondent records. Predictive model scores remain the full-sample reference unless retraining is explicitly run.

Choose a Question to Review

Select a service area, service stage, and survey question. Advanced factor settings are optional.

Question-level result
These selectors apply to every tab on this page. For results across all seven service areas, open the Cross-Service Predictor Summary.

Per-Question (Indicator-Level) Classification Review

Each CSIS service indicator represents a specific survey question and is classified separately. Results are grouped by evidence strength so readers can quickly see which findings are usable and which still need validation.

Currently selected question

Provision of medical and/or nutritional services to school clinics

The cards below place this question alongside other questions from the selected service stage, so its evidence strength can be compared fairly.

Awareness

How to read this result

Start with “Ready for interpretation.” Treat “Use with caution” as supporting evidence, and do not make strong claims from indicators that need stronger evidence.

Improvement path for Support to Education - Awareness
0 Ready for interpretation Use these indicators as the clearest decision-support results.
No indicators fall under this status for the selected view.
1 Use with caution Use these as exploratory patterns and compare them with descriptive evidence.
  • Scholarships and other assistance programs for students
3 Needs stronger evidence Do not overstate these indicators; they need more signal before stronger prediction claims.
  • Sports programs and activities
  • Provision of medical and/or nutritional services to school clinics
  • Alternative LearningSystem and/or otherSpecial Education Programs
Technical and methodology notes

Available data: 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.

Eligible responses: 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.

Predictor selection: 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.

Model comparison: 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.

What the Model Found

For Support to Education - Awareness, the summary combines 4 indicator-specific decision-tree model(s). Mean F1 is 70.5% and mean ROC AUC is 53.7%, 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 82.6%, indicating strong positive predictions when an indicator model predicts the positive service outcome.
  • Mean recall is 63.5%, meaning some indicator models may still miss actual positive cases, especially when recall is lower than precision.
  • Mean F1 score is 70.5%, which balances precision and recall across the indicator models.
  • Mean ROC AUC is 53.7%, showing how well the indicator models separate outcome groups across thresholds on average.
  • Top predictors currently include Grade level: Level (K-12) 1, MCA Dim1, House Ownership, Yes/No: School Type 8, HH Toilet Type. 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 is more reliable when it says a respondent group is positive, but it may miss some groups that also belong to that outcome. Use it to prioritize follow-up, not to exclude citizens from attention.

Key Influencing Factors

These factors contributed most strongly to how the model separated the survey responses. They show association, not proof of cause.
  • Provision of medical and/or nutritional services to school clinics
    Grade level: Level (K-12) 1
    0.5136
  • Provision of medical and/or nutritional services to school clinics
    MCA Dim1
    0.313
  • Provision of medical and/or nutritional services to school clinics
    House Ownership
    0.1456
  • Sports programs and activities
    Yes/No: School Type 8
    0.4586
  • Sports programs and activities
    MCA Dim1
    0.1896
  • Sports programs and activities
    HH Toilet Type
    0.1544
  • Scholarships and other assistance programs for students
    Grade level: Level (K-12) 1
    0.1926
  • Scholarships and other assistance programs for students
    HH Toilet Type
    0.1682

Key-Factor Strength Chart

Show how strongly each available factor influenced the model result.

Optional detail

Key Influencing Factors

Decision Tree Interpretation

Read the tree together with its plain-language insights.

Predictors shown in the simplified tree MCA Dim1 House Ownership Source of Information Civil Status
Simplified decision tree visualization

Evidence Tables

Show readiness review, indicator metrics, rule reference, and diagnostics.

Optional detail

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
Moderate signal The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. Yes / Positive (77.3%) 4350 77.3% 70.7% -6.6 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 Support to Education.
E6_EBSii
Sports programs and activities
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (84.9%) 4200 84.9% 64.3% -20.6 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 Support to Education.
E6_EBSi
Provision of medical and/or nutritional services to school clinics
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 (80.6%) 4350 80.6% 59.2% -21.4 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 Support to Education.
E6_EBSiv
Alternative LearningSystem and/or otherSpecial Education Programs
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (83.4%) 4050 83.4% 41.9% -41.6 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 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 70.5%, which gives the most balanced quick reading because it considers both correct positive predictions and missed positive cases.

Average ROC AUC is 53.7%, 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.

Precision is high at 82.6%, but recall is lower at 63.5%. Positive predictions may be reliable, but the model may still miss some actual positive cases.

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 70.7% 79.9% 82.9% 81.4% 56.4% 80.0% 79.3% all 17 19 36 3 10 entropy
E6_EBSii Sports programs and activities 64.3% 84.8% 70.6% 77.0% 50.5% 72.1% 76.5% 16 17 19 36 6 35 gini
E6_EBSi Provision of medical and/or nutritional services to school clinics 59.2% 82.9% 62.3% 71.1% 54.6% 69.6% 76.5% 8 17 19 36 3 35 gini
E6_EBSiv Alternative LearningSystem and/or otherSpecial Education Programs 41.9% 82.8% 38.2% 52.3% 53.2% 66.2% 72.0% 16 17 19 36 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
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.

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 Dim1 <= -0.691 0.4851 594 0.6 0.4 Not Aware
2 Split Dim1 <= -0.828 0.4898 552 0.6 0.4 Not Aware
3 Leaf Prediction 0.4997 144 0.5 0.5 Not Aware
4 Leaf Prediction 0.4836 408 0.6 0.4 Not Aware
5 Leaf Prediction 0.3859 42 0.7 0.3 Not Aware
6 Split Dim1 <= 0.241 0.4995 3756 0.5 0.5 Aware
7 Split Dim1 <= -0.241 0.4982 2787 0.5 0.5 Aware
8 Leaf Prediction 0.4998 2006 0.5 0.5 Aware
9 Leaf Prediction 0.4849 781 0.4 0.6 Aware
10 Split Dim1 <= 1.725 0.499 969 0.5 0.5 Not Aware
11 Leaf Prediction 0.492 659 0.6 0.4 Not Aware
12 Leaf Prediction 0.4873 310 0.4 0.6 Aware