The following code demonstrate the use of python Scikit-learn to analyze/categorize the iris data set used commonly in machine learning. This post also highlight several of the methods and modules available for various machine learning studies. While the code is not very lengthy, it did cover quite a comprehensive area as below: Data preprocessing: data […]

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SelectKBest selects the top k features that have maximum relevance with the target variable. It takes two parameters as input arguments, k (obviously) and the score function to rate the relevance of every feature with the target variable. For example, for a regression problem, you can supply feature_selection.f_regression and for a classification problem, you can […]

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scikit-learnsign predictionscikitlearnEdwin Chenscikit-learnpython Edwin Chenscikit-learnpythonscikit-learnUser Guidemachine learningReference MNnumpy X = data[:, 1:] select columns 1 through end y = data[:, 0] select column 0, the stock price from sklearn.datasets import load_svmlight_file X_train, y_train = load_svmlight_file(/path/to/train_dataset.txt) X_train.todense() S2. Supervised Classification from sklearn.linear_model import LogisticRegression clf2 = LogisticRegression().fit(X, y) LogisticRegression(C=1.0, intercept_scaling=1, dual=False, fit_intercept=True, clf2.predict_proba(X_new) array([[ 9.07512928e-01, 9.24770379e-02, […]

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Just trying to do a simple linear regression but Im baffed by this error for: These selections must have the same dimensions, and they should be numpy arrays, so what am I missing? It looks like sklearn requires the data shape of (row number, column number). If your data shape is (row number, ) like […]

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How to Create Classification and Regression Trees in Python for How to Create a Supervised Learning Model with Logistic Regression Data Journalism: Collecting Data for Your Story Data Journalism: How to Develop, Tell, and Present the Story Data Journalism: Why the Story Matters How to Create Classification and Regression Trees in Python for Data Science […]

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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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provide a useful layer of abstraction for building complex estimators or classification models. Its purpose is to aggregate a number of data transformation steps, and a model operating on the result of these transformations, into a single object that can then be used in place of a simple estimator. This allows for the one-off definition […]

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L1 Penalty and Sparsity in Logistic Regression Ordinary Least S…Ordinary Least SquaresUp This documentation is for scikit-learnversion 0.11-gitOther versions If you use the software, please considerciting scikit-learn. Python source code:plot_logistic_path.py Author: Alexandre Gramfort alexandre. Computing regularization path … © 20102011, scikit-learn developers (BSD License). Created usingSphinx1.1.2. Design byWeb y Limonada.

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How to Create a Supervised Learning Model with Logistic Regression Data Journalism: Collecting Data for Your Story Data Journalism: How to Develop, Tell, and Present the Story Data Journalism: Why the Story Matters After you build your first classification predictive model for analysis of the data, creating more models like it is a really straightforward […]

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n QueryO2OQueryO2OO2O classifierQuery0Queryo2o1Queryo2o QueryO2Ofeature100% O2O QueryQueryQueryperplexityQueryO2OQueryO2OQueryPV)(ctr)Query Querypythonnumpypythonmatplotlib010011python 10binnumpyuniversal function QueryQueryQueryO2OQueryQueryO2O SVM//k/ train data(test data)traintest datatrain datatest dataevaluate performance logistic regression accuracy: decision tree accuracy: random forest accuracy: gradient boosting accuracy: svm classifier accuracy: O2OSVM82% pythonpicklescikit-learn QueryO2OQueryQueryQueryQueryQueryQuery QueryUTF-8loadpython 3.3numpysavetxtUTF-8Query20Querybytes

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I am trying to optimize a logistic regression function in scikit-learn by using a cross-validated grid parameter search, but I cant seem to implement it. It says that Logistic Regression does not implement a get_params() but on the documentation it says it does. How can I go about optimizing this function on my ground truth? […]

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tFtF 1 npnpnp 2 OLS 1 AIC,BIC,Cp,R2 2shrinkage method regularizationridge regressionlasso0 (3) PCRPLSpmmp.lassoelastic net -lambda p0,0lambdalasso() lasso[1],[2]lasso lasso[3][3] lambdalambdalambda elastic netl1l2lasso elastic netpn, elastic netalpha1elastic netlassoelastic netlassoalpha0100lasso glmnetLasso and elastic-net regularized generalized linear models Friedman, J., Hastie, T. and Tibshirani, R cyclical coordinate descent linear regression,logistic and multinomial regression models, poisson regression the Cox modell1lassol2 […]

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From Linear Regression to Logistic Regression Binary classification with logistic regression response value represents a probablity, between [0,1] 1 . bell curve 3 . link function 4 . identity link function, 5 . 6 . The response variable is modeled as a function of a linear combination of the explanatory variables using thelogistic functionthe logistic […]

Read More → From Linear Regression to Logistic Regression

Lets take a look at a classification problem using few common regression techniques. We will start with logistic regression. CV score and std dev of scores dont look too bad. Lets take a look how C and score changes depending on the fold. C is averaged over the labels that Logistic Regresion was fit in. […]

Read More → Classification with logistic regression

scikit-learnLogistic3LogisticRegression LogisticRegressionCV logistic_regression_pathLogisticRegressionLogisticRegressionCVLogisticRegressionCVCLogisticRegressionC LogisticRegressionLogisticRegressionCVlogistic_regression_pathlogistic_regression_pathscikit-learnRandomizedLogisticRegressionLogisticRegressionL1LogisticRegressionLogisticRegressionCV 2. LogisticRegressionLogisticRegressionCV1). penaltyLogisticRegressionLogisticRegressionCVpenaltyl1l2L1L2L2penaltyL2L2L1penaltysolverL24newton-cglbfgsliblinearsagpenaltyL1liblinearL1newton-cglbfgssagliblinear 2). solversolverLogistic4a). liblinearliblinearb). lbfgsc). newton-cgd). sagnewton-cglbfgssagL1L2liblinearL1L2sag10sagsagL1L1L2newton-cglbfgssagliblinearliblinearLogisticLogisticLogisticLogisticone-vs-rest(OvR)many-vs-many(MvM)MvMOvRliblinearOvRMvMLogisticliblinearLogisticL1 3). multi_classmulti_classovrmultinomial ovrovrone-vs-rest(OvR)multinomialmany-vs-many(MvM)LogisticovrmultinomialLogisticOvRLogisticKKKLogisticKMvMMvMone-vs-one(OvO)TTT1T2T1T2T1T2LogisticT(T-1)/2OvROvRMvMOvRovr4newton-cglbfgsliblinearsagmultinomialnewton-cglbfgssag 4). class_weightclass_weightbalanced01class_weight=0:0.9, 1:0.1090%110%class_weightbalancedclass_weight100009995599.95%balanced sample_weightclass_weight 5). sample_weightclass_weightbalancedfitsample_weightscikit-learnLogisticclass_weight*sample_weight scikit-learnLogisticC Csmax_iter 3. Mnistscikit-learnLogistic—————————————————————————————————from time import timefrom sklearn.linear_model import LogisticRegression, LogisticRegressionCVfrom sklearn import metricsimport numpy as npimport mnistimport roc if __name__ == __main__: MnistmnistSet = mnist.loadLecunMnistSet()train_X, train_Y, test_X, test_Y = mnistSet[0], mnistSet[1], mnistSet[2], […]

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= 0, text: sharingCounters.facebookCount style=display: none; Share carefully! Anyone whogets this link can view the document and pass along the link. Share carefully! Anyone whogets this link can view the collection and pass along the link. Only users who sign in with a work or school account from your organization can view it. How does […]

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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 […]

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Statistics PhD Student and BIDS Fellow at UC Berkeley This question is related to my last blog post aboutwhat people consider when choosing which Python package to use.Say I want to use some statistical method. I have a few options. I could code it up from scratch myself, knowing that this might have undetected bugs […]

Read More → Which logistic regression method in Python should I use?

w1,w2…wp coef_w0intercept_ from sklearn import linear_model reg = linear_model.LinearRegression() reg.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2]) LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False) beitaw XXTX0 Multicollinearity 1000 from sklearn import linear_model reg = linear_model.Ridge (alpha = .5) reg.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1]) Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None, normalize=False, random_state=None, solver=auto, tol=0.001) […]

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@jone4291, . sklearn. sklearnclassifier, GridSearchCV + GroupKFoldparameter tuning. , Logistic regression + GroupKFold. MyData = Datawarehouse()MyData.read_data() MyLR = linear_model.LogisticRegression(n_jobs=8, fit_intercept=True) grid_search = GridSearchCV(MyLR, parameters, n_jobs=8, cv=gkf, verbose=1) grid_search.fit(MyData.train_in, MyData.train_out, MyData.group) print(Best score: %0.9f % grid_search.best_score_) for param_name in sorted(parameters.keys()): print(\t%s: %r % (param_name, best_parameters[param_name])) , cross validation, . parameter grid(sklearnParameterGrid), function evaluation. @jone4291, . group […]

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