Set the parameters of the estimator.

		Args:
			bias (array-like) : bias of the estimator. Also known as the intercept in a linear model.
			weights (array-like) : weights of the features. Also known as coeficients.
			NER biases (array-like) : NER entities infering column position on X and bias value. Ex: `b_4=10, b_5=6`.

		Example:
			>>> cls = VTT()
			>>> cls.set_params(b_4=10, b_5=6, b_6=8)

def set_params(self, **params):
		""" Set the parameters of the estimator.

		Args:
			bias (array-like) : bias of the estimator. Also known as the intercept in a linear model.
			weights (array-like) : weights of the features. Also known as coeficients.
			NER biases (array-like) : NER entities infering column position on X and bias value. Ex: `b_4=10, b_5=6`.

		Example:
			>>> cls = VTT()
			>>> cls.set_params(b_4=10, b_5=6, b_6=8)
		"""
		if 'bias' in params.keys():
			self.intercept_ = params['bias']
		if 'weights' in params.keys():
			self.coef_ = params['weights']
		for key in params.keys():
			if 'b_' == key[:2]:
				self.B[int(key[2:])] = params[key]

		return self