Feature/Depth/Sample Explorer

Feature/Depth/Sample Explorer

Explore how decision tree depth (model complexity), training sample size, and feature count affect performance (e.g., F1).

Dataset: UCI Irvine - Predict Students' Dropout and Academic Success
Target: Student is a Dropout vs. Non-dropout.

This tool is for education only.

Select features (order is preserved):

Marital Status Application mode Application order Course Daytime/evening attendance Previous qualification Previous qualification (grade) Nacionality Mother's qualification Father's qualification Mother's occupation Father's occupation Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate Inflation rate GDP

Dataset size: 4,424 rows • Test size/run: 700 • Seeds: 5

Auto-depth (grid 1..50 step 5)

max_depth (used when auto-depth is OFF)

1 50

Sample size (n)

800 3724

F1 vs Features

Sample size (n)

800 3724

F1 vs Depth

Auto-depth (grid 1..50 step 5)

max_depth (used when auto-depth is OFF)

1 50

F1 vs Sample Size

Depth vs Sample Size

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