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

Vaccination for infants/children

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 Health Services - Awareness
0 Ready for interpretation Use these indicators as the clearest decision-support results.
No indicators fall under this status for the selected view.
4 Use with caution Use these as exploratory patterns and compare them with descriptive evidence.
  • Pre-natal/post-natal/child birth services
  • Family Planning/ Reproductive Health Distribution of reproductive health supplies, information dissemination and other services
  • Free Basic Medicine or Low-Cost Medicine Program
  • Basic dental/oral hygiene
3 Needs stronger evidence Do not overstate these indicators; they need more signal before stronger prediction claims.
  • Free General Consultations/Access to secondary and/or tertiary health care
  • Prevention and Management of Communicable and Non-Communicable Diseases
  • Vaccination for infants/children
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 Health Services - Awareness, the summary combines 7 indicator-specific decision-tree model(s). Mean F1 is 73.3% and mean ROC AUC is 56.1%, 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 86.6%, indicating strong positive predictions when an indicator model predicts the positive service outcome.
  • Mean recall is 65.8%, meaning some indicator models may still miss actual positive cases, especially when recall is lower than precision.
  • Mean F1 score is 73.3%, which balances precision and recall across the indicator models.
  • Mean ROC AUC is 56.1%, showing how well the indicator models separate outcome groups across thresholds on average.
  • Top predictors currently include MCA DimMagnitude, Age Group, Source of Information, MCA Dim1, Relationship to HH Head. 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 6 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.

Key Influencing Factors

These factors contributed most strongly to how the model separated the survey responses. They show association, not proof of cause.
  • Vaccination for infants/children
    MCA DimMagnitude
    0.3959
  • Vaccination for infants/children
    Age Group
    0.1781
  • Vaccination for infants/children
    Source of Information
    0.1693
  • Pre-natal/post-natal/child birth services
    MCA Dim1
    0.4704
  • Pre-natal/post-natal/child birth services
    Relationship to HH Head
    0.3262
  • Pre-natal/post-natal/child birth services
    Exploring Additional Public Health Facilities for Follow-Up Care
    0.0931
  • Free General Consultations/Access to secondary and/or tertiary health care
    MCA Dim1
    0.4095
  • Free General Consultations/Access to secondary and/or tertiary health care
    Relationship to HH Head
    0.1469

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 Age Group Source of Information MCA Dim2
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
D8_DHSii
Pre-natal/post-natal/child birth 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 (92.8%) 4350 92.8% 77.8% -14.9 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 Health Services.
D8_DHSiii
Free General Consultations/Access to secondary and/or tertiary health care
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (87.7%) 4200 87.7% 73.1% -14.5 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 Health Services.
D8_DHSvii
Family Planning/ Reproductive Health Distribution of reproductive health supplies, information dissemination and other 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 (86.2%) 4500 86.2% 67.9% -18.3 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 Health Services.
D8_DHSv
Prevention and Management of Communicable and Non-Communicable Diseases
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 (65.6%) 4500 65.6% 59.9% -5.7 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 Health Services.
D8_DHSiv
Free Basic Medicine or Low-Cost Medicine Program
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% 53.8% -33.9 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 Health Services.
D8_DHSvi
Basic dental/oral hygiene
Moderate signal The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. Yes / Positive (77.2%) 3450 77.2% 54.9% -22.2 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 Health Services.
D8_DHSi
Vaccination for infants/children
Needs more predictors The model has difficulty separating respondents based on ROC AUC, recall, or F1. Yes / Positive (95.2%) 4500 95.2% 44.2% -51.1 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 Health Services.

Indicator-Level Metrics

How the mean scores are formed

The AVG values summarize 7 indicator model(s) for Health Services - Awareness. They describe the overall pattern across indicators, not the result of one specific indicator.

Average F1 is 73.3%, which gives the most balanced quick reading because it considers both correct positive predictions and missed positive cases.

Average ROC AUC is 56.1%, 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 86.6%, and recall is 65.8%. 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
D8_DHSii Pre-natal/post-natal/child birth services 77.8% 93.3% 82.0% 87.3% 55.4% 80.7% 85.9% 16 17 6 23 3 35 gini
D8_DHSiii Free General Consultations/Access to secondary and/or tertiary health care 73.1% 88.2% 80.1% 83.9% 51.8% 73.8% 80.8% 16 17 6 23 5 10 entropy
D8_DHSvii Family Planning/ Reproductive Health Distribution of reproductive health supplies, information dissemination and other services 67.9% 88.7% 72.0% 79.5% 57.8% 78.4% 84.3% 8 17 6 23 3 35 gini
D8_DHSv Prevention and Management of Communicable and Non-Communicable Diseases 59.9% 66.9% 77.1% 71.6% 52.3% 58.9% 70.7% 8 17 6 23 3 10 gini
D8_DHSiv Free Basic Medicine or Low-Cost Medicine Program 53.8% 90.3% 52.9% 66.8% 56.4% 60.9% 68.4% 8 17 6 23 5 35 gini
D8_DHSvi Basic dental/oral hygiene 54.9% 81.5% 53.8% 64.8% 57.4% 64.7% 75.8% 16 17 6 23 3 10 entropy
D8_DHSi Vaccination for infants/children 44.2% 97.2% 42.6% 59.2% 61.5% 78.4% 84.9% all 17 6 23 3 10 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
A3.1 Age Group 1=18→24; 2=25→29; 3=30→34; 4=35→39; 5=40→44; 6=45→54; 7=55→64; 8=65→74; 9=75 and above <= 7.5 means 18→24, 25→29, 30→34, 35→39, 40→44, 45→54, 55→64; > 7.5 means 65→74, 75 and above.
Dim2 MCA profile dimension 2 A combined respondent-profile score from MCA. It is not a single survey question. Dim2 <= 0.434 follows one side of the profile map; values above 0.434 follow the other side.
B6 Source of Information 1=TELEVISION; 2=RADIO; 3=NEWSPAPER; 4=FAMILY/FRIENDS; 5=INTERNET; 6=MUNICIPAL GOVERNMENT; 7=BARANGAY OFFICIALS AND PERSONNEL; 99=OTHERS <= 5.5 means TELEVISION, RADIO, NEWSPAPER, FAMILY/FRIENDS, INTERNET; > 5.5 means MUNICIPAL GOVERNMENT, BARANGAY OFFICIALS AND PERSONNEL, OTHERS.

Gini Split Diagnostics

Node Type Rule Gini Samples Not Aware Aware Prediction
0 Split A3.1 <= 7.500 0.5 4500 0.5 0.5 Aware
1 Split A3.1 <= 1.500 0.4978 3896 0.5 0.5 Aware
2 Split Dim2 <= 0.434 0.4571 526 0.6 0.4 Not Aware
3 Leaf Prediction 0.4213 386 0.7 0.3 Not Aware
4 Leaf Prediction 0.4666 140 0.4 0.6 Aware
5 Split B6 <= 5.500 0.4881 3370 0.4 0.6 Aware
6 Leaf Prediction 0.4994 1865 0.5 0.5 Aware
7 Leaf Prediction 0.4417 1505 0.3 0.7 Aware
8 Split Dim2 <= 0.830 0.4557 604 0.6 0.4 Not Aware
9 Split B6 <= 2.500 0.4979 238 0.5 0.5 Aware
10 Leaf Prediction 0.4858 122 0.6 0.4 Not Aware
11 Leaf Prediction 0.3848 116 0.3 0.7 Aware
12 Split Dim2 <= 1.212 0.4063 366 0.7 0.3 Not Aware
13 Leaf Prediction 0.2882 89 0.8 0.2 Not Aware
14 Leaf Prediction 0.4519 277 0.7 0.3 Not Aware