Python Packages

[tsoiadelina]

Inputs (for each func)

• [x] array of predictors (np.array) (add assert for type np.ndarray)
• [x] array of the predicted variable (add add assert for type np.ndarray)
• [x] type of regulators (l1, l2, none)

  Output (for each func)

• [x] function in analytical form (is it necessary?)
• [x] weights of each x (yea)
• [x] finding a free regression term (is it necessary?)

  Functions

  reference: https://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares

Loss:

L1:

L2:

• [x] Linear regression with L1
• [x] Linear regression with L2
• [x] Polynomial with L1

1. Make polynomial features with sklearn: PolynomialFeatures
2. Make linear regression on this features
   • [x] Polynomial with L2

exponential:

Let's make THETA = [THETA0, *THETA1] is m dimensional vector-column. And try:

1. minimize sse loss without logarithmic
2. minimize after logarithmic
   • [x] Exponential with L1
   • [x] Exponential with L2

[tsoiadelina]

How should I use methods here?

[vktrbr]

algorithms