ML model visualizer

Linear Regression

Fit best line through data points and watch gradient descent learning.

TypeRegressionLossMSEOptimizerGradient DescentOutputContinuous
Example PointsFormat: (x, y), (x, y). Use 2-30 points.
0.00010.02
1800

Visualization

Epoch 0 / 100
0.01.73.45.16.98.60.01.93.75.67.59.3XY
Data pointsRegression lineError

Ready to optimize slope and intercept with gradient descent.

Metrics

Epoch0 / 100
MSE24.279
R² Score-5.460
Slope (m)0.000
Intercept (b)0.000
Learning rate0.01

Loss Curve

EpochLoss0 / 100

Training Steps

  1. Predict y = m x + b
  2. Compute error (prediction - actual)
  3. Calculate MSE
  4. Compute gradients for m and b
  5. Update parameters
  6. Return trained model
Current equationy = 0.000x + 0.000
Prediction for x = 60.000
StatusReady