Regressor

[source]

BCNRegressor

BCN.BCNRegressor.BCNRegressor(
    B=10,
    nu=0.4,
    col_sample=1,
    lam=0.1,
    r=0.9,
    tol=0,
    type_optim="nlminb",
    activation="sigmoid",
    hidden_layer_bias=True,
    verbose=0,
    show_progress=True,
    seed=123,
)

BCN (Boosted Configuration Networks) regression model

Parameters:

B:  int
    Number of iterations of the algorithm.  
nu: float
    Learning rate.
col_sample: float
    Percentage of columns (covariates) adjusted at each iteration of the algorithm.
lam: float
    Defines lower and upper bounds neural networks weights.
r: float
    With 0 < r < 1. Controls the convergence rate of residuals.
tol: float
    Convergence tolerance for an early stopping
type_optim: string
    Type of optimization procedure used for finding neural networks weights at each iteration ("nlminb", "nmkb", "hjkb", "bobyqa", "randomsearch")
activation: string
    Activation function (must be bounded). Currently: "sigmoid", "tanh".
hidden_layer_bias: boolean
    If there is a bias parameter in neural networks weights. If yes, True (default). 
verbose: int
    Controls verbosity (for checks). The higher, the more verbose.
show_progress: boolean
    If True, a progress bar is displayed.
seed: int
    For reproducibility of results.

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[source]

fit

BCNRegressor.fit(X, y, **kwargs)

Fit BCN (Boosted Configuration Networks) regression model

Parameters:

X: {ndarray} of shape (n_samples, n_features)
    Training data.

y: ndarray of shape (n_samples,) 
    Target values.

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[source]

predict

BCNRegressor.predict(X, **kwargs)

Predict using BCN (Boosted Configuration Networks) regression model

Parameters:

X: array-like, shape (n_samples, n_features)
    Test data.

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