Model Regresi Linear
Model: $y = m \cdot x + c$ (slope dan
intercept)
Loss Function: $L = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$ (Mean Squared Error)
Loss Function: $L = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$ (Mean Squared Error)
Gradient Descent Otomatis Dua Parameter
Lihat bagaimana algoritma secara otomatis menyesuaikan slope dan intercept untuk meminimalkan loss:
0.10
0.50
0.00
0.50
0.00
Current Loss (MSE):
0.000
Siap memulai algoritma gradient descent
Data Points & Garis Regresi
Permukaan Loss 3D & Jalur Gradient Descent
Informasi Model Dua Parameter
Persamaan Model Extended:
$y = m \cdot x + c$
Partial Derivatives:
$\frac{\partial L}{\partial m} = \frac{2}{n} \sum_{i=1}^{n} (\hat{y}_i - y_i) \cdot x_i$
$\frac{\partial L}{\partial c} = \frac{2}{n} \sum_{i=1}^{n} (\hat{y}_i - y_i)$
Iterasi Saat Ini: 0
Parameter Saat Ini: m = 0.500, c = 0.000
Gradient Saat Ini: ∂L/∂m = 0.000, ∂L/∂c = 0.000
$y = m \cdot x + c$
Partial Derivatives:
$\frac{\partial L}{\partial m} = \frac{2}{n} \sum_{i=1}^{n} (\hat{y}_i - y_i) \cdot x_i$
$\frac{\partial L}{\partial c} = \frac{2}{n} \sum_{i=1}^{n} (\hat{y}_i - y_i)$
Iterasi Saat Ini: 0
Parameter Saat Ini: m = 0.500, c = 0.000
Gradient Saat Ini: ∂L/∂m = 0.000, ∂L/∂c = 0.000
| # | Nilai X | Nilai Y (True) | Y Prediksi | Loss (Squared Error) |
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