机器学习实战 | 第三章：集成学习

集成学习肯定是在实战中最不可或缺的思想了.毕竟都想把错误率低一点,再低一点,再低一点.看看kaggle大量的集成学习就知道这节肯定绕不过去了.

在这里，仅仅说一下最基本的bagging的类，至于更加具体的随机森林或者boosting方法会具体的开一篇来写。bagging有两个类，一个是BaggingClassifier，还有一个是BaggingRegressor，两种形式都是类似的，所以之详细说BaggingClassifier，另外一个类比就行。

class sklearn.ensemble.BaggingClassifier(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)

参数: base_estimator : 一个对象或者None，默认是None，这里是传入一个基本的学习器对象，比如Ridge对象啊，等等。要是None的话，学习器就是决策树。 n_estimators : int类型,表示基本学习器的数量。默认是10 max_samples : int类型或者float类型, 默认为1.0. 这个参数表示从数据集X中抽出多少的数据用来训练基本的学习器。当为整数的时候，就抽出整数个样本，当为浮点数的时候，就抽出该比例的样本。 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 : bool Whether to use out-of-bag samples to estimate the generalization error. 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 ensemble. New in version 0.17: warm_start constructor parameter. 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 used by np.random. verbose : int, optional (default=0) Controls the verbosity of the building process.

属性

base_estimator_ : estimator The base estimator from which the ensemble is grown. estimators_ : list of estimators The collection of fitted base estimators. estimators_samples_ : list of arrays The subset of drawn samples (i.e., the in-bag samples) for each base estimator. Each subset is defined by a boolean mask. estimators_features_ : list of arrays The subset of drawn features for each base estimator. classes_ : array of shape = [n_classes] The classes labels. n_classes_ : int or list The number of classes. 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.

Methods decision_function(*args, **kwargs) Average of the decision functions of the base classifiers. fit(X, y[, sample_weight]) Build a Bagging ensemble of estimators from the training set (X, y). get_params([deep]) Get parameters for this estimator. predict(X) Predict class for X. predict_log_proba(X) Predict class log-probabilities for X. predict_proba(X) Predict class probabilities for X. score(X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of this estimator. init(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)[source] decision_function(*args, **kwargs)[source] Average of the decision functions of the base classifiers. Parameters: X : {array-like, sparse matrix} of shape = [n_samples, n_features] The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns: score : array, shape = [n_samples, k] The decision function of the input samples. The columns correspond to the classes in sorted order, as they appear in the attribute classes_. Regression and binary classification are special cases with k == 1, otherwise k==n_classes. estimators_samples_ The subset of drawn samples for each base estimator. Returns a dynamically generated list of boolean masks identifying the samples used for for fitting each member of the ensemble, i.e., the in-bag samples. Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected. fit(X, y, sample_weight=None)[source] Build a Bagging ensemble of estimators from the training set (X, y).

Parameters: X : {array-like, sparse matrix} of shape = [n_samples, n_features] The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. y : array-like, shape = [n_samples] The target values (class labels in classification, real numbers in regression). sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Note that this is supported only if the base estimator supports sample weighting.

Returns: self : object Returns self. get_params(deep=True)[source] Get parameters for this estimator. Parameters: deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns: params : mapping of string to any Parameter names mapped to their values. predict(X)[source] Predict class for X. The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a predict_proba method, then it resorts to voting.

Parameters: X : {array-like, sparse matrix} of shape = [n_samples, n_features] The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns: y : array of shape = [n_samples] The predicted classes. predict_log_proba(X)[source] Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the base 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 if they are supported by the base estimator.

Returns: p : array of shape = [n_samples, n_classes] The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_. predict_proba(X)[source] Predict class probabilities for X. The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a predict_proba method, then it resorts to voting and the predicted class probabilities of an input sample represents the proportion of estimators predicting each class.

Parameters: X : {array-like, sparse matrix} of shape = [n_samples, n_features] The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.

Returns: p : array of shape = [n_samples, n_classes] The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_. score(X, y, sample_weight=None)[source] Returns the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters: X : array-like, shape = (n_samples, n_features) Test samples. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights.

Returns: score : float Mean accuracy of self.predict(X) wrt. y. set_params(**params)[source] Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such