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

Barangay roads

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 Public Works and Infrastructure - Awareness
0 Ready for interpretation Use these indicators as the clearest decision-support results.
No indicators fall under this status for the selected view.
2 Use with caution Use these as exploratory patterns and compare them with descriptive evidence.
  • Public markets and satellite markets
  • Barangay hall
10 Needs stronger evidence Do not overstate these indicators; they need more signal before stronger prediction claims.
  • Barangay roads
  • Public Cemetery
  • Sports centers and facilities
  • Road Safety
  • Municipal Government Buildings
  • Flood Control Management System
  • Public parks and open spaces
  • Multipurpose halls or civic centers
  • Municipal roads and bridges
  • Information and reading center
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 Public Works and Infrastructure - Awareness, the summary combines 12 indicator-specific decision-tree model(s). Mean F1 is 62.0% and mean ROC AUC is 55.0%, 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 77.3%, indicating strong when an indicator model predicts the positive service outcome.
  • Mean recall is 55.1%, meaning some indicator models may still miss actual positive cases, especially when recall is lower than precision.
  • Mean F1 score is 62.0%, which balances precision and recall across the indicator models.
  • Mean ROC AUC is 55.0%, showing how well the indicator models separate outcome groups across thresholds on average.
  • Top predictors currently include MCA DimMagnitude, MCA Dim2, Source of Drinking Water, MCA Dim1, Source of Information. These variables are useful signals for explaining which respondent profiles are linked to the selected service outcome.
  • 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.
  • Barangay roads
    MCA DimMagnitude
    0.6163
  • Barangay roads
    MCA Dim2
    0.2792
  • Barangay roads
    Source of Drinking Water
    0.1046
  • Municipal roads and bridges
    MCA DimMagnitude
    0.361
  • Municipal roads and bridges
    MCA Dim1
    0.2868
  • Municipal roads and bridges
    MCA Dim2
    0.0919
  • Barangay hall
    Source of Information
    0.2577
  • Barangay hall
    MCA Dim1
    0.1786

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 Dim2 Source of Drinking Water MCACluster
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
H1_HPIi_A
Barangay roads
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 (93.9%) 4500 93.9% 91.8% -2.0 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 Public Works and Infrastructure.
H1_HPIx
Public Cemetery
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (85.7%) 4200 85.7% 69.0% -16.6 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 Public Works and Infrastructure.
H1_HPIvii
Sports centers and facilities
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 (76.3%) 4200 76.3% 58.1% -18.2 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 Public Works and Infrastructure.
H1_HPIvi
Road Safety
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 (64.4%) 4500 64.4% 56.0% -8.4 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 Public Works and Infrastructure.
H1_HPIiv
Public markets and satellite markets
Moderate signal The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. Yes / Positive (76.1%) 4200 76.1% 54.3% -21.8 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 Public Works and Infrastructure.
H1_HPIii
Barangay hall
Moderate signal The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. Yes / Positive (91.3%) 3900 91.3% 48.5% -42.8 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 Public Works and Infrastructure.
H1_HPIix
Municipal Government Buildings
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (85.6%) 4200 85.6% 49.3% -36.3 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 Public Works and Infrastructure.
H1_HPIxi
Flood Control Management System
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (65.8%) 4350 65.8% 53.9% -11.9 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 Public Works and Infrastructure.
H1_HPIv
Public parks and open spaces
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (59.1%) 4050 59.1% 55.4% -3.7 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 Public Works and Infrastructure.
H1_HPIiii
Multipurpose halls or civic centers
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (76.3%) 3900 76.3% 43.8% -32.5 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 Public Works and Infrastructure.
H1_HPIi_B
Municipal roads and bridges
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (89.1%) 4200 89.1% 33.0% -56.0 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 Public Works and Infrastructure.
H1_HPIviii
Information and reading center
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. No / Negative (70.7%) 2850 70.7% 51.1% -19.6 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 Public Works and Infrastructure.

Indicator-Level Metrics

How the mean scores are formed

The AVG values summarize 12 indicator model(s) for Public Works and Infrastructure - Awareness. They describe the overall pattern across indicators, not the result of one specific indicator.

Average F1 is 62.0%, which gives the most balanced quick reading because it considers both correct positive predictions and missed positive cases.

Average ROC AUC is 55.0%, 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.

Precision is high at 77.3%, but recall is lower at 55.1%. 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
H1_HPIi_A Barangay roads 91.8% 94.1% 97.4% 95.7% 52.0% 59.8% 83.9% all 17 0 17 3 10 entropy
H1_HPIx Public Cemetery 69.0% 85.7% 76.7% 80.9% 51.1% 69.9% 82.2% 16 17 0 17 5 35 entropy
H1_HPIvii Sports centers and facilities 58.1% 79.7% 60.4% 68.8% 53.8% 65.1% 69.3% all 17 0 17 3 35 entropy
H1_HPIvi Road Safety 56.0% 67.1% 62.2% 64.6% 54.9% 63.2% 64.8% all 17 0 17 5 35 entropy
H1_HPIiv Public markets and satellite markets 54.3% 80.9% 52.3% 63.5% 56.0% 68.4% 74.4% 8 17 0 17 3 10 entropy
H1_HPIii Barangay hall 48.5% 94.3% 46.4% 62.2% 60.8% 70.6% 79.2% all 17 0 17 5 35 entropy
H1_HPIix Municipal Government Buildings 49.3% 87.7% 47.5% 61.6% 53.6% 62.8% 63.0% 16 17 0 17 6 35 entropy
H1_HPIxi Flood Control Management System 53.9% 71.7% 49.4% 58.5% 57.9% 64.1% 64.2% 8 17 0 17 6 10 entropy
H1_HPIv Public parks and open spaces 55.4% 68.1% 46.2% 55.1% 59.5% 62.6% 64.4% 8 17 0 17 6 10 entropy
H1_HPIiii Multipurpose halls or civic centers 43.8% 77.6% 36.9% 50.0% 51.8% 59.8% 60.4% 8 17 0 17 6 35 gini
H1_HPIi_B Municipal roads and bridges 33.0% 89.5% 28.1% 42.8% 53.3% 73.9% 74.5% all 17 0 17 5 10 entropy
H1_HPIviii Information and reading center 51.1% 31.6% 57.4% 40.7% 55.0% 41.5% 42.2% all 17 0 17 6 35 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 <= 2.138 follows one side of the profile map; values above 2.138 follow the other side.

Gini Split Diagnostics

Node Type Rule Gini Samples Not Aware Aware Prediction
0 Split Dim2 <= 2.138 0.5 4500 0.5 0.5 Aware
1 Split Dim2 <= -1.321 0.4999 4449 0.5 0.5 Aware
2 Split Dim2 <= -1.421 0.4823 421 0.4 0.6 Aware
3 Leaf Prediction 0.4999 191 0.5 0.5 Not Aware
4 Leaf Prediction 0.4124 230 0.3 0.7 Aware
5 Split Dim2 <= -1.310 0.5 4028 0.5 0.5 Not Aware
6 Leaf Prediction 0.3285 25 0.8 0.2 Not Aware
7 Leaf Prediction 0.5 4003 0.5 0.5 Aware
8 Split Dim2 <= 2.293 0.3581 51 0.8 0.2 Not Aware
9 Leaf Prediction 0.2841 25 0.8 0.2 Not Aware
10 Leaf Prediction 0.4447 26 0.7 0.3 Not Aware