Linear Regression
The Math: (y = Xw + b)
The Loop:
Use Gradient Descent.Predict (y_{pred}).Calculate Error ((y_{pred} - y_{true})).
Compute Gradient:
(X^T \cdot \text{Error} / n).Update Weights: (w = w - (\text{learning_rate} \times \text{gradient})).
Logistic RegressionThe Difference:
Same as Linear Regression, but pass the result through the Sigmoid Function: (1 / (1 + e^{-z})).
The Goal:
Maps any number to a probability between 0 and 1.
K-Means
The Logic:
Pick (k) random points as "centroids."Assign every data point to the nearest centroid.Move centroids to the average of their assigned points.Repeat until the centroids stop moving.
Top comments (0)