Evaluasi model

Latihan Pertanyaan Wawancara Statistik di R

Zuzanna Chmielewska

Actuary

Cross-validation

Cross-validation

Cross-validation

Cross-validation

Cross-validation

Cross-validation

Cross-validation

Confusion matrix

Confusion matrix

Confusion matrix

Confusion matrix

Metrik klasifikasi

$\text{accuracy} = \frac{TP+TN}{TP+TN+FP+FN}$

$\text{precision} = \frac{TP}{TP+FP}$

$\text{recall} = \frac{TP}{TP+FN}$

Metrik klasifikasi

Precision

Recall

Metrik regresi

Metrik regresi

Metrik regresi

Root Mean Squared Error

$RMSE = \sqrt{\frac{1}{n}\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$

Mean Absolute Error

$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$

Metrik regresi

Root Mean Squared Error

$RMSE = \sqrt{\frac{1}{n}\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$

• bobot tinggi untuk galat besar

Mean Absolute Error

$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$

• mudah diinterpretasikan

Ringkasan

• pendekatan validation set
• cross-validation
• confusion matrix
• metrik klasifikasi
• metrik regresi

Ayo berlatih!

Latihan Pertanyaan Wawancara Statistik di R