machine_learning.gradient_boosting_classifier
=============================================

.. py:module:: machine_learning.gradient_boosting_classifier


Attributes
----------

.. autoapisummary::

   machine_learning.gradient_boosting_classifier.iris


Classes
-------

.. autoapisummary::

   machine_learning.gradient_boosting_classifier.GradientBoostingClassifier


Module Contents
---------------

.. py:class:: GradientBoostingClassifier(n_estimators: int = 100, learning_rate: float = 0.1)

   .. py:method:: fit(features: numpy.ndarray, target: numpy.ndarray) -> None

      Fit the GradientBoostingClassifier to the training data.

      Parameters:
      - features (np.ndarray): The training features.
      - target (np.ndarray): The target values.

      Returns:
      None

      >>> import numpy as np
      >>> from sklearn.datasets import load_iris
      >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
      >>> iris = load_iris()
      >>> X, y = iris.data, iris.target
      >>> clf.fit(X, y)
      >>> # Check if the model is trained
      >>> len(clf.models) == 100
      True



   .. py:method:: gradient(target: numpy.ndarray, y_pred: numpy.ndarray) -> numpy.ndarray

      Calculate the negative gradient (pseudo-residuals) for logistic loss.

      Parameters:
      - target (np.ndarray): The target values.
      - y_pred (np.ndarray): The predicted values.

      Returns:
      - np.ndarray: An array of pseudo-residuals.

      >>> import numpy as np
      >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
      >>> target = np.array([0, 1, 0, 1])
      >>> y_pred = np.array([0.2, 0.8, 0.3, 0.7])
      >>> residuals = clf.gradient(target, y_pred)
      >>> # Check if residuals have the correct shape
      >>> residuals.shape == target.shape
      True



   .. py:method:: predict(features: numpy.ndarray) -> numpy.ndarray

      Make predictions on input data.

      Parameters:
      - features (np.ndarray): The input data for making predictions.

      Returns:
      - np.ndarray: An array of binary predictions (-1 or 1).

      >>> import numpy as np
      >>> from sklearn.datasets import load_iris
      >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
      >>> iris = load_iris()
      >>> X, y = iris.data, iris.target
      >>> clf.fit(X, y)
      >>> y_pred = clf.predict(X)
      >>> # Check if the predictions have the correct shape
      >>> y_pred.shape == y.shape
      True



   .. py:attribute:: learning_rate
      :value: 0.1



   .. py:attribute:: models
      :type:  list[tuple[sklearn.tree.DecisionTreeRegressor, float]]
      :value: []



   .. py:attribute:: n_estimators
      :value: 100



.. py:data:: iris