ML之RF&XGBoost：基于RF/XGBoost(均+5f-CrVa)算法对Titanic(泰坦尼克号)数据集进行二分类预测(乘客是否生还)

ML之RF&XGBoost：基于RF/XGBoost(均+5f-CrVa)算法对Titanic(泰坦尼克号)数据集进行二分类预测(乘客是否生还)

目录

输出结果

比赛结果

设计思路

核心代码

输出结果

比赛结果

设计思路

核心代码

    rfc = RandomForestClassifier()          
    rfc_cross_val_score=cross_val_score(rfc, X_train, y_train, cv=5).mean()
    print('RF：',rfc_cross_val_score)       
 
    rfc.fit(X_train,y_train)
    rfc_y_predict = rfc.predict(X_test)
    rfc_submission = pd.DataFrame({'PassengerId': test['PassengerId'], 'Survived': rfc_y_predict})
    rfc_submission.to_csv('data_input/Titanic Data/Titanic_rfc_submission.csv', index=False)
    
 
 
    xgbc = XGBClassifier()                   
    xgbc_cross_val_score=cross_val_score(xgbc, X_train, y_train, cv=5).mean()
    print('XGBoost：',xgbc_cross_val_score)  
 
    xgbc.fit(X_train, y_train)
    xgbc_y_predict = xgbc.predict(X_test)
    xgbc_submission = pd.DataFrame({'PassengerId': test['PassengerId'], 'Survived': xgbc_y_predict})
    xgbc_submission.to_csv('data_input/Titanic Data/Titanic_xgbc_submission.csv', index=False)

class RandomForestClassifier(-title class_ inherited__">ForestClassifier):
    """A random forest classifier.
    
    A random forest is a meta estimator that fits a number of decision tree
    classifiers on various sub-samples of the dataset and use averaging to
    improve the predictive accuracy and control over-fitting.
    The sub-sample size is always the same as the original
    input sample size but the samples are drawn with replacement if
    `bootstrap=True` (default).
    
    Read more in the :ref:`User Guide <forest>`.
    
    Parameters
    ----------
    n_estimators : integer, optional (default=10)
    The number of trees in the forest.
    
    criterion : string, optional (default="gini")
    The function to measure the quality of a split. Supported criteria are
    "gini" for the Gini impurity and "entropy" for the information gain.
    Note: this parameter is tree-specific.
    
    max_features : int, float, string or None, optional (default="auto")
    The number of features to consider when looking for the best split:
    
    - If int, then consider `max_features` features at each split.
    - If float, then `max_features` is a percentage and
    `int(max_features * n_features)` features are considered at each
    split.
    - If "auto", then `max_features=sqrt(n_features)`.
    - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
    - If "log2", then `max_features=log2(n_features)`.
    - If None, then `max_features=n_features`.
    
    Note: the search for a split does not stop until at least one
    valid partition of the node samples is found, even if it requires to
    effectively inspect more than ``max_features`` features.
    
    max_depth : integer or None, optional (default=None)
    The maximum depth of the tree. If None, then nodes are expanded 
     until
    all leaves are pure or until all leaves contain less than
    min_samples_split samples.
    
    min_samples_split : int, float, optional (default=2)
    The minimum number of samples required to split an internal node:
    
    - If int, then consider `min_samples_split` as the minimum number.
    - If float, then `min_samples_split` is a percentage and
    `ceil(min_samples_split * n_samples)` are the minimum
    number of samples for each split.
    
    .. versionchanged:: 0.18
    Added float values for percentages.
    
    min_samples_leaf : int, float, optional (default=1)
    The minimum number of samples required to be at a leaf node:
    
    - If int, then consider `min_samples_leaf` as the minimum number.
    - If float, then `min_samples_leaf` is a percentage and
    `ceil(min_samples_leaf * n_samples)` are the minimum
    number of samples for each node.
    
    .. versionchanged:: 0.18
    Added float values for percentages.
    
    min_weight_fraction_leaf : float, optional (default=0.)
    The minimum weighted fraction of the sum total of weights (of all
    the input samples) required to be at a leaf node. Samples have
    equal weight when sample_weight is not provided.
    
    max_leaf_nodes : int or None, optional (default=None)
    Grow trees with ``max_leaf_nodes`` in best-first fashion.
    Best nodes are defined as relative reduction in impurity.
    If None then unlimited number of leaf nodes.
    
    min_impurity_split : float,
    Threshold for early stopping in tree growth. A node will split
    if its impurity is above the threshold, otherwise it is a leaf.
    
    .. deprecated:: 0.19
    ``min_impurity_split`` has been deprecated in favor of
    ``min_impurity_decrease`` in 0.19 and will be removed in 0.21.
    Use ``min_impurity_decrease`` instead.
    
    min_impurity_decrease : float, optional (default=0.)
    A node will be split if this split induces a decrease of the impurity
    greater than or equal to this value.
    
    The weighted impurity decrease equation is the following::
    
    N_t / N * (impurity - N_t_R / N_t * right_impurity
    - N_t_L / N_t * left_impurity)
    
    where ``N`` is the total number of samples, ``N_t`` is the number of
    samples at the current node, ``N_t_L`` is the number of samples in the
    left child, and ``N_t_R`` is the number of samples in the right child.
    
    ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
    if ``sample_weight`` is passed.
    
    .. versionadded:: 0.19
    
    bootstrap : boolean, optional (default=True)
    Whether bootstrap samples are used when building trees.
    
    oob_score : bool (default=False)
    Whether to use out-of-bag samples to estimate
    the generalization accuracy.
    
    n_jobs : integer, optional (default=1)
    The number of jobs to run in parallel for both `fit` and `predict`.
    If -1, then the number of jobs is set to the number of cores.
    
    random_state : int, RandomState instance or None, optional 
     (default=None)
    If int, random_state is the seed used by the random number 
     generator;
    If RandomState instance, random_state is the random number 
     generator;
    If None, the random number generator is the RandomState instance 
     used
    by `np.random`.
    
    verbose : int, optional (default=0)
    Controls the verbosity of the tree building process.
    
    warm_start : bool, optional (default=False)
    When set to ``True``, reuse the solution of the previous call to fit
    and add more estimators to the ensemble, otherwise, just fit a whole
    new forest.
    
    class_weight : dict, list of dicts, "balanced",
    "balanced_subsample" or None, optional (default=None)
    Weights associated with classes in the form ``{class_label: weight}``.
    If not given, all classes are supposed to have weight one. For
    multi-output problems, a list of dicts can be provided in the same
    order as the columns of y.
    
    Note that for multioutput (including multilabel) weights should be
    defined for each class of every column in its own dict. For example,
    for four-class multilabel classification weights should be
    [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
    [{1:1}, {2:5}, {3:1}, {4:1}].
    
    The "balanced" mode uses the values of y to automatically adjust
    weights inversely proportional to class frequencies in the input data
    as ``n_samples / (n_classes * np.bincount(y))``
    
    The "balanced_subsample" mode is the same as "balanced" except 
     that
    weights are computed based on the bootstrap sample for every tree
    grown.
    
    For multi-output, the weights of each column of y will be multiplied.
    
    Note that these weights will be multiplied with sample_weight 
     (passed
    through the fit method) if sample_weight is specified.
    
    Attributes
    ----------
    estimators_ : list of DecisionTreeClassifier
    The collection of fitted sub-estimators.
    
    classes_ : array of shape = [n_classes] or a list of such arrays
    The classes labels (single output problem), or a list of arrays of
    class labels (multi-output problem).
    
    n_classes_ : int or list
    The number of classes (single output problem), or a list containing the
    number of classes for each output (multi-output problem).
    
    n_features_ : int
    The number of features when ``fit`` is performed.
    
    n_outputs_ : int
    The number of outputs when ``fit`` is performed.
    
    feature_importances_ : array of shape = [n_features]
    The feature importances (the higher, the more important the feature).
    
    oob_score_ : float
    Score of the training dataset obtained using an out-of-bag estimate.
    
    oob_decision_function_ : array of shape = [n_samples, n_classes]
    Decision function computed with out-of-bag estimate on the training
    set. If n_estimators is small it might be possible that a data point
    was never left out during the bootstrap. In this case,
    `oob_decision_function_` might contain NaN.
    
    Examples
    --------
    >>> from sklearn.ensemble import RandomForestClassifier
    >>> from sklearn.datasets import make_classification
    >>>
    >>> X, y = make_classification(n_samples=1000, n_features=4,
    ...                            n_informative=2, n_redundant=0,
    ...                            random_state=0, shuffle=False)
    >>> clf = RandomForestClassifier(max_depth=2, random_state=0)
    >>> clf.fit(X, y)
    RandomForestClassifier(bootstrap=True, class_weight=None, 
     criterion='gini',
    max_depth=2, max_features='auto', max_leaf_nodes=None,
    min_impurity_decrease=0.0, min_impurity_split=None,
    min_samples_leaf=1, min_samples_split=2,
    min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
    oob_score=False, random_state=0, verbose=0, warm_start=False)
    >>> print(clf.feature_importances_)
    [ 0.17287856  0.80608704  0.01884792  0.00218648]
    >>> print(clf.predict([[0, 0, 0, 0]]))
    [1]
    
    Notes
    -----
    The default values for the parameters controlling the size of the trees
    (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and
    unpruned trees which can potentially be very large on some data 
     sets. To
    reduce memory consumption, the complexity and size of the trees 
     should be
    controlled by setting those parameter values.