tick.linear_model.ModelLinReg¶

class tick.linear_model.ModelLinReg(fit_intercept: bool = True, n_threads: int = 1)[source]¶

Least-squares loss for linear regression. This class gives first order information (gradient and loss) for this model and can be passed to any solver through the solver’s set_model method.

Given training data \((x_i, y_i) \in \mathbb R^d \times \mathbb R\) for \(i=1, \ldots, n\), this model considers a goodness-of-fit

\[f(w, b) = \frac 1n \sum_{i=1}^n \ell(y_i, b + x_i^\top w),\]

where \(w \in \mathbb R^d\) is a vector containing the model-weights, \(b \in \mathbb R\) is the intercept (used only whenever fit_intercept=True) and \(\ell : \mathbb R^2 \rightarrow \mathbb R\) is the loss given by

\[\ell(y, y') = \frac 12 (y - y')^2\]

for \(y, y' \in \mathbb R\). Data is passed to this model through the fit(X, y) method where X is the features matrix (dense or sparse) and y is the vector of labels.

Parameters:

fit_intercept : bool

If True, the model uses an intercept

Attributes:

features : {numpy.ndarray, scipy.sparse.csr_matrix}, shape=(n_samples, n_features)

The features matrix, either dense or sparse

labels : numpy.ndarray, shape=(n_samples,) (read-only)

The labels vector

n_samples : int (read-only)

Number of samples

n_features : int (read-only)

Number of features

n_coeffs : int (read-only)

Total number of coefficients of the model

dtype : {'float64', 'float32'}

Type of the data arrays used.

n_threads : int, default=1 (read-only)

Number of threads used for parallel computation.

• if int <= 0: the number of threads available on the CPU

• otherwise the desired number of threads

Examples using tick.linear_model.ModelLinReg¶