fit (X, y) [source] ¶. Fit Gaussian process regression model. Parameters X array-like of shape (n_samples, n_features) or list of object. Feature vectors or other representations of training data. y array-like of shape (n_samples,) or (n_samples, n_targets). Target values. Returns self object. GaussianProcessRegressor class instance.
The kernel used for prediction. The structure of the kernel is the same as the one passed as parameter but with optimized hyperparameters. L_array-like of shape ...
14.12.2021 · I think that this is expected. CompoundKernel is used internally for classification: we will train a kernel per class in the multiclass setting and we store them in a CompoundKernel.. In regression, you don't have such behaviour: you have a single kernel and a single estimator. I think what you try to do is to combine the kernel and for this purpose, you only need to use the …
An array with shape (n_features, ) or (1, ). The parameters in the autocorrelation model. If thetaL and thetaU are also specified, theta0 is considered as the ...
15.12.2018 · sklearn.mixture.GMM is no longer available in current versions of sklearn, as per the documentation: Deprecated since version 0.18: This class will be removed in 0.20. Use sklearn.mixture.GaussianMixture instead. Given that, I believe your options are either to change the code to use GaussianMixture or downgrade your version of sklearn.
1.7.1. Gaussian Process Regression (GPR) ¶. The GaussianProcessRegressor implements Gaussian processes (GP) for regression purposes. For this, the prior of the GP needs to be specified. The prior mean is assumed to be constant and zero (for normalize_y=False) or the training data’s mean (for normalize_y=True ).
can't use scikit-learn - "AttributeError: 'module' object has no attribute ..." I'm trying to follow this tutorial of scikit-learn (linear regression).