Machine Learning · FIB-UPC
Machine Learning — Hypothyroid
Explore an end-to-end hypothyroidism classification pipeline with 9 sklearn model families. The interactive demos below run real trained models directly in your browser.
Clean
Imputation, encoding, scaling
Train
9 sklearn model families
Evaluate
CV, ROC, confusion matrices
Explain
Feature importance and error analysis
LogRegLinear SVCk-NNDecision TreeRandom ForestAdaBoostMLPNaive BayesQDA
Hypothyroid predictor
Real LogReg model · runs in browser
Age50 years
TSH2 mIU/L
TT4109 nmol/L
T32 nmol/L
Negative — healthy
Logistic Regression · 97.3% confidence
k-NN on PCA projection
200 real test-set points · click to classify
k =
Click the plot to classify a point
Feature importance
Age0.11
TSH5.42
TT40.76
T30.12
|coeff| from Logistic Regression on standardized features — TSH dominates prediction.
Dataset summary
hypothyroid.arff — UCI-style with numeric (age, TSH, T3, TT4, FTI, T4U) and categorical attributes. Target: binaryClass (P/N).
Challenges: heavy NaN, outliers in age, dropped TBG, class imbalance — handled via imputation, appropriate metrics (F1, ROC-AUC).
▸ Jupyter notebook preview
Open in Nbviewer▸ Run the notebooks locally
git clone https://github.com/cuberhaus/APA_Practica.git cd APA_Practica python -m venv .venv && source .venv/bin/activate pip install -r requirements.txt jupyter lab PracticaAPA-Hipotiroidismo-PolCasacubertaMartaGranero.ipynb