EL之Bagging：kaggle比賽之利用泰坦尼克號數(shù)據(jù)集建立Bagging模型對每個人進行獲救是否預測

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目錄

輸出結(jié)果

設(shè)計思路

核心代碼

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輸出結(jié)果

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設(shè)計思路

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核心代碼

bagging_clf = BaggingRegressor(clf_LoR, n_estimators=10, max_samples=0.8, max_features=1.0, bootstrap=True, bootstrap_features=False, n_jobs=-1)bagging_clf.fit(X, y)#BaggingRegressor class BaggingRegressor Found at: sklearn.ensemble.baggingclass BaggingRegressor(BaseBagging, RegressorMixin):"""A Bagging regressor.A Bagging regressor is an ensemble meta-estimator that fits baseregressors each on random subsets of the original dataset and thenaggregate their individual predictions (either by voting or by averaging)to form a final prediction. Such a meta-estimator can typically be used asa way to reduce the variance of a black-box estimator (e.g., a decisiontree), by introducing randomization into its construction procedure andthen making an ensemble out of it.This algorithm encompasses several works from the literature. When randomsubsets of the dataset are drawn as random subsets of the samples, thenthis algorithm is known as Pasting [1]_. If samples are drawn withreplacement, then the method is known as Bagging [2]_. When random subsetsof the dataset are drawn as random subsets of the features, then the methodis known as Random Subspaces [3]_. Finally, when base estimators are builton subsets of both samples and features, then the method is known asRandom Patches [4]_.Read more in the :ref:`User Guide <bagging>`.Parameters----------base_estimator : object or None, optional (default=None)The base estimator to fit on random subsets of the dataset.If None, then the base estimator is a decision tree.n_estimators : int, optional (default=10)The number of base estimators in the ensemble.max_samples : int or float, optional (default=1.0)The number of samples to draw from X to train each base estimator.- If int, then draw `max_samples` samples.- If float, then draw `max_samples * X.shape[0]` samples.max_features : int or float, optional (default=1.0)The number of features to draw from X to train each base estimator.- If int, then draw `max_features` features.- If float, then draw `max_features * X.shape[1]` features.bootstrap : boolean, optional (default=True)Whether samples are drawn with replacement.bootstrap_features : boolean, optional (default=False)Whether features are drawn with replacement.oob_score : boolWhether to use out-of-bag samples to estimatethe generalization error.warm_start : bool, optional (default=False)When set to True, reuse the solution of the previous call to fitand add more estimators to the ensemble, otherwise, just fita whole new ensemble.n_jobs : int, 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 usedby `np.random`.verbose : int, optional (default=0)Controls the verbosity of the building process.Attributes----------estimators_ : list of estimatorsThe collection of fitted sub-estimators.estimators_samples_ : list of arraysThe subset of drawn samples (i.e., the in-bag samples) for each baseestimator. Each subset is defined by a boolean mask.estimators_features_ : list of arraysThe subset of drawn features for each base estimator.oob_score_ : floatScore of the training dataset obtained using an out-of-bag estimate.oob_prediction_ : array of shape = [n_samples]Prediction computed with out-of-bag estimate on the trainingset. If n_estimators is small it might be possible that a data pointwas never left out during the bootstrap. In this case,`oob_prediction_` might contain NaN.References----------.. [1] L. Breiman, "Pasting small votes for classification in largedatabases and on-line", Machine Learning, 36(1), 85-103, 1999... [2] L. Breiman, "Bagging predictors", Machine Learning, 24(2), 123-140,1996... [3] T. Ho, "The random subspace method for constructing decisionforests", Pattern Analysis and Machine Intelligence, 20(8), 832-844,1998... [4] G. Louppe and P. Geurts, "Ensembles on Random Patches", MachineLearning and Knowledge Discovery in Databases, 346-361, 2012."""def __init__(self, base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=1, random_state=None, verbose=0):super(BaggingRegressor, self).__init__(base_estimator, n_estimators=n_estimators, max_samples=max_samples, max_features=max_features, bootstrap=bootstrap, bootstrap_features=bootstrap_features, oob_score=oob_score, warm_start=warm_start, n_jobs=n_jobs, random_state=random_state, verbose=verbose)def predict(self, X):"""Predict regression target for X.The predicted regression target of an input sample is computed as themean predicted regression targets of the estimators in the ensemble.Parameters----------X : {array-like, sparse matrix} of shape = [n_samples, n_features]The training input samples. Sparse matrices are accepted only ifthey are supported by the base estimator.Returns-------y : array of shape = [n_samples]The predicted values."""check_is_fitted(self, "estimators_features_")# Check dataX = check_array(X, accept_sparse=['csr', 'csc'])# Parallel loopn_jobs, n_estimators, starts = _partition_estimators(self.n_estimators, self.n_jobs)all_y_hat = Parallel(n_jobs=n_jobs, verbose=self.verbose)(delayed(_parallel_predict_regression)(self.estimators_[starts[i]:starts[i + 1]], self.estimators_features_[starts[i]:starts[i + 1]], X) for i in range(n_jobs))# Reducey_hat = sum(all_y_hat) / self.n_estimatorsreturn y_hatdef _validate_estimator(self):"""Check the estimator and set the base_estimator_ attribute."""super(BaggingRegressor, self)._validate_estimator(default=DecisionTreeRegressor())def _set_oob_score(self, X, y):n_samples = y.shape[0]predictions = np.zeros((n_samples, ))n_predictions = np.zeros((n_samples, ))for estimator, samples, features in zip(self.estimators_, self.estimators_samples_, self.estimators_features_):# Create mask for OOB samplesmask = ~samplespredictions[mask] += estimator.predict(mask:])[(X[:features])n_predictions[mask] += 1if (n_predictions == 0).any():warn("Some inputs do not have OOB scores. ""This probably means too few estimators were used ""to compute any reliable oob estimates.")n_predictions[n_predictions == 0] = 1predictions /= n_predictionsself.oob_prediction_ = predictionsself.oob_score_ = r2_score(y, predictions)

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