SGDClassifier SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.15, learning_rate=optimal, loss=hinge, n_iter=5, n_jobs=1, penalty=l2, power_t=0.5, random_state=None, shuffle=True, loss=hinge: (soft-margin) linear Support Vector Machine, loss=modified_huber: smoothed hinge loss, loss=squared_loss: Ordinary least squares, loss=huber: Huber loss for robust regression, loss=epsilon_insensitive: linear Support Vector Regression. penalty=l2: L2 norm penalty oncoef_. penalty=l1: L1 norm penalty oncoef_. penalty=elasticnet: Convex combination of […]

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Java OOPs interview questions and answers RESTful Web Service interview questions Top Java Hibernate interview questions Java interview questions, coding in a team Runtime comparison of string concatenation Remote debugging of tomcat using eclipse jConsole guide for simple connection Properly Shutting down an ExecutorService max connections in application not in sync with db max c […]

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is alinear least-squares L1-regularized regression systemwithinsklearn.linear_model(that implements aLASSO algorithmto solve aLASSO task). 1) ImportLasso Regressionmodel from scikit-learn: from sklearn.linear_model import Lasso lasso=Lasso(alpha=alpha[,fit_intercept=True, normalize=False,…]) Predict Y using thelinear modelwith estimated coefficients: Return coefficient of determination (R^2) of the prediction: lasso.score(X,Y[, sample_weight=w]) Computeelastic net pathwithcoordinate descent: lasso.path(X, y[, l1_ratio, eps, n_alphas,…]) from sklearn.cross_validation import KFold from sklearn.linear_model […]

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1.xpathtag//tag/ 2.tag*[@class=str]css.str,xpathstr*[contains(@class,str)]containsstarts-with/ends-with… scrapyspiderxpath/cssselector pywin32dllsytem32 pip install pypiwin32scrapy pip install scrapyScrapy(pip,Python) AttributeError: module object has no attribute SSL_ST_INIT pip install pyopenssl pip uninstall pyopenssl CPUCPUcppcppswitch case 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, solver=liblinear, max_iter=100, multi_class=ovr, verbose=0, warm_start=False, n_jobs=1) LogisticRegression(logit,MaxEnt)classifier… sklearn.preprocessing.PolynomialFeatures class sklearn.preprocessing.PolynomialFeatures(degree=2, interaction_only=False, include_bias=True) Generate a new feature matrix consisting of all polynomial […]

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Sign uporlog into customize your list. Start here for a quick overview of the site Detailed answers to any questions you might have Discuss the workings and policies of this site Learn more about Stack Overflow the company Learn more about hiring developers or posting ads with us Join Stack Overflowto learn, share knowledge, and […]

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Java OOPs interview questions and answers RESTful Web Service interview questions Top Java Hibernate interview questions Java interview questions, coding in a team Runtime comparison of string concatenation Remote debugging of tomcat using eclipse jConsole guide for simple connection Properly Shutting down an ExecutorService max connections in application not in sync with db max c […]

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Scikit sklearn. linear_model: Scikit Learn sklearn.linear_model.LinearRegression: View the results of the model generated , sklearn. linear_model-, matplotlib , . , (B0,B1)r , , import numpy as p import pandas as pn from sklearn import datasets, linear_model z = pn.DataFrame( a : [1,2,3,4,5,6,7,8,9], b : [9,8,7,6,5,4,3,2,1] ) a2 = z[a].values.reshape(9,1) b2 = z[b].values.reshape(9,1) reg = linear_model.LinearRegression(fit_intercept=True) […]

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sklearnsklearn sklearnIRISIRISFisher19364Sepal.LengthSepal.WidthPetal.LengthPetal.WidthIris SetosaIris VersicolourIris VirginicaIRIS 10 NNii10 sklearnpreproccessing [0, 1] preproccessingStandardScaler preproccessingMinMaxScaler z-scorel2 preproccessingNormalizer 10 preproccessingBinarizer IRISpreproccessingOneHotEncoder IRIS4NaNpreproccessingImputer 42 preproccessingPolynomialFeatures preproccessingFunctionTransformer Filter Wrapper EmbeddedFilter sklearnfeature_selection feature_selectionVarianceThreshold Pfeature_selectionSelectKBest NMij feature_selectionSelectKBest feature_selectionSelectKBest feature_selectionRFE feature_selectionSelectFromModelL1 L1L2L11L2L10L1 feature_selectionSelectFromModelL1L2 GBDTfeature_selectionSelectFromModelGBDT L1PCALDAPCALDAPCALDAPCALDAPCALDA sklearnfit_transformfit_transformfit_transformfitfitsklearn SVD Actually the sucess of all Machine Learning algorithms depends on how you present the data. Better feature […]

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KDnuggets HomeNews2016FebSoftware Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn (16:n06)Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn http likes 413Tags:Deep LearningGoogleMatthew MayoPythonscikit-learnTensorFlow Scikit Learn is a new easy-to-use interface for TensorFlow from Google based on the Scikit-learn fit/predict model. Does it succeed in making deep learning more accessible? GooglesTensorFlowhas been publicly available […]

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sklearn import numpy as np from sklearn import metrics from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import SGDClassifier from sklearn.svm import LinearSVC from sklearn.utils.extmath import density categories = [ alt.atheism, talk.religion.misc, comp.graphics, sci.space, ] print(Loading 20 newsgroups dataset for categories:) print(categories if categories else all) dataHome = /Users/xinsheng/PycharmProjects/PythonPlaygroud/dataset data_train = fetch_20newsgroups(data_home= dataHome, […]

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sklearn.grid_search.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch=2*n_jobs, error_score=raise) GridSearchCVfitpredictpredict_proba from __future__ import print_function from pprint import pprint from time import time import logging from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.linear_model import SGDClassifier from sklearn.grid_search import GridSearchCV from sklearn.pipeline import Pipeline print(__doc__) Display progress logs on […]

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Sign uporlog into customize your list. Start here for a quick overview of the site Detailed answers to any questions you might have Discuss the workings and policies of this site Learn more about Stack Overflow the company Learn more about hiring developers or posting ads with us Ask Ubuntu is a question and answer […]

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Java OOPs interview questions and answers RESTful Web Service interview questions Top Java Hibernate interview questions Java interview questions, coding in a team Runtime comparison of string concatenation Remote debugging of tomcat using eclipse jConsole guide for simple connection Properly Shutting down an ExecutorService max connections in application not in sync with db max c […]

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This documentation is for scikit-learn If you use the software, please considerciting scikit-learn. Randomized Lasso works by resampling the train data and computing a Lasso on each resampling. In short, the features selected more often are good features. It is also known as stability selection. alpha: float, aic, or bic, optional The regularization parameter alpha […]

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self.sess.run(tf.global_variables_initializer()) initial = tf.truncated_normal(shape,stddev = self._index_in_epoch self._num_datas: self._datas[start:end],self._labels[start:end] self.x = tf.placeholder(tf.float32, [ self.y = tf.placeholder(tf.float32,[ self.w = self.weight_variable([self.x_dimen, self.y_prec = tf.nn.bias_add(tf.matmul(self.x, self.w), self.b) mse = tf.reduce_mean(tf.squared_difference(self.y_prec, self.y)) l2 = tf.reduce_mean(tf.square(self.w)) self.train_step = tf.train.AdamOptimizer( (self,x_train,y_train,x_test,y_test) self.sess.run(self.train_step,feed_dict=self.x:batch[ train_loss = self.sess.run(self.loss,feed_dict=self.x:batch[ pred = self.sess.run(self.y_prec, self.x:x_test_batch) x_train,x_test,y_train, y_test = train_test_split(x, y, test_size= linear.train(x_train, y_lrm_train,x_test,y_lrm_test) , r2_score(y_predict.ravel(), y_lrm_test.ravel())) y_predict = […]

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3.1.1. Computing cross-validated metrics 3.1.1.1. Obtaining predictions by cross-validation 3.1.2.5. Leave-One-Label-Out – LOLO 3.1.2.7. Random permutations cross-validation a.k.a. Shuffle & Split 3.1.2.8. Predefined Fold-Splits / Validation-Sets 3.1.4. Cross validation and model selection 3.2. Grid Search: Searching for estimator parameters 3.2.2. Randomized Parameter Optimization 3.2.3.1. Specifying an objective metric 3.2.3.2. Composite estimators and parameter spaces 3.2.3.3. […]

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scikit-learnJiancheng Li. Linear regression with combined L1 and L2 priors as regularizer. If you are interested in controlling the L1 and L2 penalty separately, keep in mind that this is equivalent to: The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. Specifically, l1_ratio = […]

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is a collection oflinear model regression systemswithinscikit-learn. is the name of the selectedgeneralized linear model regression system. sklearn.linear_model.LinearRegression() sklearn.linear_model.ARDRegression() sklearn.linear_model.BayesianRidge() , aBayesian Ridge Regression System. sklearn.linear_model.ElasticNetCV() , anElasticNet Cross-Validation System. sklearn.linear_model.HuberRegressor() , aLeast Angle Regression Cross-Validation System. , aLASSO-LARS Cross-Validation System‎. , aLASSO-LARS Information Criteria System. sklearn.linear_model.LinearRegression() , anOrdinary Least-Squares Linear Regression System. sklearn.linear_model.MultiTaskLasso() sklearn.linear_model.TheilSenRegressor() […]

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Code Style from The Hitchhikers Guide sklearn SVM classifier xiamx 2016-03-09 03:40:06 +08:00 4591 640 2016-03-09 09:10:50 +08:00 via Android sklearn SVM numpy openblas spark 2016-03-09 13:56:04 +08:00 via iPhone sklearn svm wrap libsvm liblinear SVM 2016-03-09 13:59:00 +08:00 via iPhone sklearn.linear_model.SGDClassifier(loss=hinge) VERSION: 3.9.8.0 55ms UTC 16:19 PVG 00:19 LAX 08:19 JFK 11:19 ♥ Do […]

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GitHub is home to over 20 million developers working together to host and review code, manage projects, and build software together. Have a question about this project?Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub?Sign into your account sklearn 0.14.1, and it happens […]

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