ML之LoRBaggingRF：依次利用LoR、Bagging、RF算法对泰坦尼克号数据集 (Kaggle经典案例)获救人员进行二分类预测(最全)

ML之LoR&Bagging&RF：依次利用LoR、Bagging、RF算法对泰坦尼克号数据集 (Kaggle经典案例)获救人员进行二分类预测

输出结果

设计思路

核心代码

输出结果

1、数据集可视化以及统计分析

2、优化baseline模型

ML之LoR&Bagging&RF：依次利用LoR、Bagging、RF算法对泰坦尼克号数据集 (Kaggle经典案例)获救人员进行二分类预测——优化baseline模型

3、模型融合

ML之LoR&Bagging&RF：依次利用Bagging、RF算法对泰坦尼克号数据集 (Kaggle经典案例)获救人员进行二分类预测——模型融合

设计思路

核心代码

LoR算法

clf_LoR = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)
clf_LoR.fit(X, y)#LoR算法
class LogisticRegression Found at: sklearn.linear_model.logisticclass LogisticRegression(BaseEstimator, LinearClassifierMixin, SparseCoefMixin):"""Logistic Regression (aka logit, MaxEnt) classifier.In the multiclass case, the training algorithm uses the one-vs-rest (OvR)scheme if the 'multi_class' option is set to 'ovr', and uses the cross-entropy loss if the 'multi_class' option is set to 'multinomial'.(Currently the 'multinomial' option is supported only by the 'lbfgs','sag' and 'newton-cg' solvers.)This class implements regularized logistic regression using the'liblinear' library, 'newton-cg', 'sag' and 'lbfgs' solvers. It can handleboth dense and sparse input. Use C-ordered arrays or CSR matricescontaining 64-bit floats for optimal performance; any other input formatwill be converted (and copied).The 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularizationwith primal formulation. The 'liblinear' solver supports both L1 and L2regularization, with a dual formulation only for the L2 penalty.Read more in the :ref:`User Guide <logistic_regression>`.Parameters----------penalty : str, 'l1' or 'l2', default: 'l2'Used to specify the norm used in the penalization. The 'newton-cg','sag' and 'lbfgs' solvers support only l2 penalties... versionadded:: 0.19l1 penalty with SAGA solver (allowing 'multinomial' + L1)dual : bool, default: FalseDual or primal formulation. Dual formulation is only implemented forl2 penalty with liblinear solver. Prefer dual=False whenn_samples > n_features.tol : float, default: 1e-4Tolerance for stopping criteria.C : float, default: 1.0Inverse of regularization strength; must be a positive float.Like in support vector machines, smaller values specify strongerregularization.fit_intercept : bool, default: TrueSpecifies if a constant (a.k.a. bias or intercept) should beadded to the decision function.intercept_scaling : float, default 1.Useful only when the solver 'liblinear' is usedand self.fit_intercept is set to True. In this case, x becomes[x, self.intercept_scaling],i.e. a "synthetic" feature with constant value equal tointercept_scaling is appended to the instance vector.The intercept becomes ``intercept_scaling * synthetic_feature_weight``.Note! the synthetic feature weight is subject to l1/l2 regularizationas all other features.To lessen the effect of regularization on synthetic feature weight(and therefore on the intercept) intercept_scaling has to be increased.class_weight : dict or 'balanced', default: NoneWeights associated with classes in the form ``{class_label: weight}``.If not given, all classes are supposed to have weight one.The "balanced" mode uses the values of y to automatically adjustweights inversely proportional to class frequencies in the input dataas ``n_samples / (n_classes * np.bincount(y))``.Note that these weights will be multiplied with sample_weight (passedthrough the fit method) if sample_weight is specified... versionadded:: 0.17*class_weight='balanced'*random_state : int, RandomState instance or None, optional, default: NoneThe seed of the pseudo random number generator to use when shufflingthe data.  If int, random_state is the seed used by the random numbergenerator; If RandomState instance, random_state is the random numbergenerator; If None, the random number generator is the RandomStateinstance used by `np.random`. Used when ``solver`` == 'sag' or'liblinear'.solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'},default: 'liblinear'Algorithm to use in the optimization problem.- For small datasets, 'liblinear' is a good choice, whereas 'sag' and'saga' are faster for large ones.- For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'handle multinomial loss; 'liblinear' is limited to one-versus-restschemes.- 'newton-cg', 'lbfgs' and 'sag' only handle L2 penalty, whereas'liblinear' and 'saga' handle L1 penalty.Note that 'sag' and 'saga' fast convergence is only guaranteed onfeatures with approximately the same scale. You canpreprocess the data with a scaler from sklearn.preprocessing... versionadded:: 0.17Stochastic Average Gradient descent solver... versionadded:: 0.19SAGA solver.max_iter : int, default: 100Useful only for the newton-cg, sag and lbfgs solvers.Maximum number of iterations taken for the solvers to converge.multi_class : str, {'ovr', 'multinomial'}, default: 'ovr'Multiclass option can be either 'ovr' or 'multinomial'. If the optionchosen is 'ovr', then a binary problem is fit for each label. Elsethe loss minimised is the multinomial loss fit acrossthe entire probability distribution. Does not work for liblinearsolver... versionadded:: 0.18Stochastic Average Gradient descent solver for 'multinomial' case.verbose : int, default: 0For the liblinear and lbfgs solvers set verbose to any positivenumber for verbosity.warm_start : bool, default: FalseWhen set to True, reuse the solution of the previous call to fit asinitialization, otherwise, just erase the previous solution.Useless for liblinear solver... versionadded:: 0.17*warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.n_jobs : int, default: 1Number of CPU cores used when parallelizing over classes ifmulti_class='ovr'". This parameter is ignored when the ``solver``is setto 'liblinear' regardless of whether 'multi_class' is specified ornot. If given a value of -1, all cores are used.Attributes----------coef_ : array, shape (1, n_features) or (n_classes, n_features)Coefficient of the features in the decision function.`coef_` is of shape (1, n_features) when the given problemis binary.intercept_ : array, shape (1,) or (n_classes,)Intercept (a.k.a. bias) added to the decision function.If `fit_intercept` is set to False, the intercept is set to zero.`intercept_` is of shape(1,) when the problem is binary.n_iter_ : array, shape (n_classes,) or (1, )Actual number of iterations for all classes. If binary or multinomial,it returns only 1 element. For liblinear solver, only the maximumnumber of iteration across all classes is given.See also--------SGDClassifier : incrementally trained logistic regression (when giventhe parameter ``loss="log"``).sklearn.svm.LinearSVC : learns SVM models using the same algorithm.Notes-----The underlying C implementation uses a random number generator toselect features when fitting the model. It is thus not uncommon,to have slightly different results for the same input data. Ifthat happens, try with a smaller tol parameter.Predict output may not match that of standalone liblinear in certaincases. See :ref:`differences from liblinear <liblinear_differences>`in the narrative documentation.References----------LIBLINEAR -- A Library for Large Linear Classificationhttp://www.csie.ntu.edu.tw/~cjlin/liblinear/SAG -- Mark Schmidt, Nicolas Le Roux, and Francis BachMinimizing Finite Sums with the Stochastic Average Gradienthttps://hal.inria.fr/hal-00860051/documentSAGA -- Defazio, A., Bach F. & Lacoste-Julien S. (2014).SAGA: A Fast Incremental Gradient Method With Supportfor Non-Strongly Convex Composite Objectiveshttps://arxiv.org/abs/1407.0202Hsiang-Fu Yu, Fang-Lan Huang, Chih-Jen Lin (2011). Dual coordinate descentmethods for logistic regression and maximum entropy models.Machine Learning 85(1-2):41-75.http://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf"""def __init__(self, penalty='l2', dual=False, tol=1e-4, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver='liblinear', max_iter=100, multi_class='ovr', verbose=0, warm_start=False, n_jobs=1):self.penalty = penaltyself.dual = dualself.tol = tolself.C = Cself.fit_intercept = fit_interceptself.intercept_scaling = intercept_scalingself.class_weight = class_weightself.random_state = random_stateself.solver = solverself.max_iter = max_iterself.multi_class = multi_classself.verbose = verboseself.warm_start = warm_startself.n_jobs = n_jobsdef fit(self, X, y, sample_weight=None):"""Fit the model according to the given training data.Parameters----------X : {array-like, sparse matrix}, shape (n_samples, n_features)Training vector, where n_samples is the number of samples andn_features is the number of features.y : array-like, shape (n_samples,)Target vector relative to X.sample_weight : array-like, shape (n_samples,) optionalArray of weights that are assigned to individual samples.If not provided, then each sample is given unit weight... versionadded:: 0.17*sample_weight* support to LogisticRegression.Returns-------self : objectReturns self."""if not isinstance(self.C, numbers.Number) or self.C < 0:raise ValueError("Penalty term must be positive; got (C=%r)" % self.C)if not isinstance(self.max_iter, numbers.Number) or self.max_iter < 0:raise ValueError("Maximum number of iteration must be positive;"" got (max_iter=%r)" % self.max_iter)if not isinstance(self.tol, numbers.Number) or self.tol < 0:raise ValueError("Tolerance for stopping criteria must be ""positive; got (tol=%r)" % self.tol)if self.solver in ['newton-cg']:_dtype = [np.float64, np.float32]else:_dtype = np.float64X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order="C")check_classification_targets(y)self.classes_ = np.unique(y)n_samples, n_features = X.shape_check_solver_option(self.solver, self.multi_class, self.penalty, self.dual)if self.solver == 'liblinear':if self.n_jobs != 1:warnings.warn("'n_jobs' > 1 does not have any effect when"" 'solver' is set to 'liblinear'. Got 'n_jobs'"" = {}.".format(self.n_jobs))self.coef_, self.intercept_, n_iter_ = _fit_liblinear(X, y, self.C, self.fit_intercept, self.intercept_scaling, self.class_weight, self.penalty, self.dual, self.verbose, self.max_iter, self.tol, self.random_state, sample_weight=sample_weight)self.n_iter_ = np.array([n_iter_])return selfif self.solver in ['sag', 'saga']:max_squared_sum = row_norms(X, squared=True).max()else:max_squared_sum = Nonen_classes = len(self.classes_)classes_ = self.classes_if n_classes < 2:raise ValueError("This solver needs samples of at least 2 classes"" in the data, but the data contains only one"" class: %r" % classes_[0])if len(self.classes_) == 2:n_classes = 1classes_ = classes_[1:]if self.warm_start:warm_start_coef = getattr(self, 'coef_', None)else:warm_start_coef = Noneif warm_start_coef is not None and self.fit_intercept:warm_start_coef = np.append(warm_start_coef, self.intercept_[:np.newaxis], axis=1)self.coef_ = list()self.intercept_ = np.zeros(n_classes)# Hack so that we iterate only once for the multinomial case.if self.multi_class == 'multinomial':classes_ = [None]warm_start_coef = [warm_start_coef]if warm_start_coef is None:warm_start_coef = [None] * n_classespath_func = delayed(logistic_regression_path)# The SAG solver releases the GIL so it's more efficient to use# threads for this solver.if self.solver in ['sag', 'saga']:backend = 'threading'else:backend = 'multiprocessing'fold_coefs_ = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, backend=backend)(path_func(X, y, pos_class=class_, Cs=[self.C], fit_intercept=self.fit_intercept, tol=self.tol, verbose=self.verbose, solver=self.solver, multi_class=self.multi_class, max_iter=self.max_iter, class_weight=self.class_weight, check_input=False, random_state=self.random_state, coef=warm_start_coef_, penalty=self.penalty, max_squared_sum=max_squared_sum, sample_weight=sample_weight) for (class_, warm_start_coef_) in zip(classes_, warm_start_coef))fold_coefs_, _, n_iter_ = zip(*fold_coefs_)self.n_iter_ = np.asarray(n_iter_, dtype=np.int32)[:0]if self.multi_class == 'multinomial':self.coef_ = fold_coefs_[0][0]else:self.coef_ = np.asarray(fold_coefs_)self.coef_ = self.coef_.reshape(n_classes, n_features + int(self.fit_intercept))if self.fit_intercept:self.intercept_ = self.coef_[:-1]self.coef_ = self.coef_[::-1]return selfdef predict_proba(self, X):"""Probability estimates.The returned estimates for all classes are ordered by thelabel of classes.For a multi_class problem, if multi_class is set to be "multinomial"the softmax function is used to find the predicted probability ofeach class.Else use a one-vs-rest approach, i.e calculate the probabilityof each class assuming it to be positive using the logistic function.and normalize these values across all the classes.Parameters----------X : array-like, shape = [n_samples, n_features]Returns-------T : array-like, shape = [n_samples, n_classes]Returns the probability of the sample for each class in the model,where classes are ordered as they are in ``self.classes_``."""if not hasattr(self, "coef_"):raise NotFittedError("Call fit before prediction")calculate_ovr = self.coef_.shape[0] == 1 or self.multi_class == "ovr"if calculate_ovr:return super(LogisticRegression, self)._predict_proba_lr(X)else:return softmax(self.decision_function(X), copy=False)def predict_log_proba(self, X):"""Log of probability estimates.The returned estimates for all classes are ordered by thelabel of classes.Parameters----------X : array-like, shape = [n_samples, n_features]Returns-------T : array-like, shape = [n_samples, n_classes]Returns the log-probability of the sample for each class in themodel, where classes are ordered as they are in ``self.classes_``."""return np.log(self.predict_proba(X))

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输出结果

设计思路

核心代码

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