python 机器学习之sklearn-LR

from numpy import * 
from sklearn.datasets import load_iris     # import datasets

# load the dataset: iris
iris = load_iris() 
samples = iris.data
#print samples 
target = iris.target 

# import the LogisticRegression
from sklearn.linear_model import LogisticRegression 

classifier = LogisticRegression()  # 使用类，参数全是默认的
classifier.fit(samples, target)  # 训练数据来学习，不需要返回值

x = classifier.predict([5, 3, 5, 2.5])  # 测试数据，分类返回标记

print x 

sklearn.linear_model.LogisticRegression

class  sklearn.linear_model. LogisticRegression ( penalty='l2',  dual=False,  tol=0.0001,  C=1.0,  fit_intercept=True, intercept_scaling=1,  class_weight=None,  random_state=None )

Logistic Regression (aka logit, MaxEnt) classifier.

In the multiclass case, the training algorithm uses a one-vs.-all (OvA) scheme, rather than the “true” multinomial LR.

This class implements L1 and L2 regularized logistic regression using the liblinear library. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied).

Parameters:

penalty : string, ‘l1’ or ‘l2’    惩罚项的种类 Used to specify the norm used in the penalization. dual : boolean Dual or primal formulation. Dual formulation is only implemented for l2 penalty. Prefer dual=False when n_samples > n_features. C : float, optional (default=1.0) Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization. fit_intercept : bool, default: True Specifies if a constant (a.k.a. bias or intercept) should be added the decision function. intercept_scaling : float, default: 1 when self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_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 regularization as 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, ‘auto’}, optional    考虑类不平衡，类似于代价敏感 Over-/undersamples the samples of each class according to the given weights. If not given, all classes are supposed to have weight one. The ‘auto’ mode selects weights inversely proportional to class frequencies in the training set. random_state: int seed, RandomState instance, or None (default) : The seed of the pseudo random number generator to use when shuffling the data. tol: float, optional : Tolerance for stopping criteria.

Attributes:

`coef_` : array, shape = [n_classes, n_features] Coefficient of the features in the decision function. coef_ is readonly property derived from raw_coef_ that follows the internal memory layout of liblinear. `intercept_` : array, shape = [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.

LogisticRegression类中的方法有如下几种，我们常用的是fit和predict~

Methods

decision_function(X)	Predict confidence scores for samples.
densify()	Convert coefficient matrix to dense array format.
fit(X, y)	Fit the model according to the given training data.    用来训练LR分类器，其中的X是训练样本，y是对应的标记向量
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict class labels for samples in X.    用来预测测试样本的标记，也就是分类。X是测试样本集
predict_log_proba(X)	Log of probability estimates.
predict_proba(X)	Probability estimates.
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.
sparsify()	Convert coefficient matrix to sparse format.
transform(X[, threshold])	Reduce X to its most important features.

引用：http://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model