# Tag: sklearn regression

## Իعlogisticع (linear regression and logistic regression

ϣԤYԻعģΪ ǽؾ࣬ϵԲͬԴ߶ıʽʾ֮Ľȣģڲ϶Եġ һѵÿǵiݵҪƲ ˹ƣͨƽͣRSS XȣʽΪ0õһ⣺ ͼѵɢͼGGREENREDʾ ԻعݣӦY01ֱʾGREENREDΪ Ԥdecision boundaryǣ logisticعͨxԺKĺʽģͬʱȷǵĺΪ1[0,1]ʽΪ ɼlogisticعԻعֻӳһ㺯ӳ䡣 ȻlogisticعN۲ĶȻǣ 2⡣0/1Ӧ롣ͬʱͨȻΪ 1ؾ㡣 ΪʽʹNewton-Raphson㷨Ҫ Ϊ㣬ɾʽyʾXPiԪϸWȨΪԽǾiԽԪΪô ظ⣬ΪÿPӶWZҲԸ㷨ؼȨ㷨ʽȨW logisticعҲԻعֻӳһ㺯ӳ䣻ȻlogisticģͣؼȨ㷨Ȼ LOFTERѳӡ20ƬҪ this.p= m:2, b:2, loftPermalink:, id:fks_, blogTitle:Իعlogisticع (linear regression and logistic regression), blogAbstract: ϣԤYԻعģΪ , blogTag:Իع,logisticع,,ؼȨ, blogUrl:blog/static/0826843, isPublished:1, istop:false, type:2, modifyTime:41, publishTime:43, permalink:blog/static/0826843, commentCount:5, mainCommentCount:3, recommendCount:4, bsrk:-100, publisherId:0, recomBlogHome:false, currentRecomBlog:false, attachmentsFileIds:[], vote:, groupInfo:, friendstatus:none, followstatus:unFollow, pubSucc:, visitorProvince:, […]

Read More → Իعlogisticع (linear regression and logistic regression## sklearnSVM1

Support Vector Machine Pattern Recognition and Machine Learning sklearn SVR().fit(X,Y).predict(X) ) y_rbf = svr_rbf.fit(X, y).predict(X) y_lin = svr_lin.fit(X, y).predict(X) y_poly = svr_poly.fit(X, y).predict(X) 3Separating hyperplane for unbalanced classes -recall class_weight=110class_weight=auto – Leetcode 21. Merge Two Sorted Lists(C++) LeetCode:Linked List Cycle && Linked List Cycle II sklearn-SVM3(+) sklearn-SVM2(anova ) UIDatePicker~~~ GetEnv() return code = -2JDWP exit […]

Read More → sklearnSVM1## Python_sklearnlogistic regression

%matplotlib inline import matplotlib.pyplot as plt from matplotlib.font_manager import FontProperties font=FontProperties(fname=rc:\windows\fonts\msyh.ttc, size=10) import numpy as np plt.figure() plt.axis([-6,6,0,1]) plt.grid(True) X=np.arange(-6,6,0.1) y=1/(1+np.e**(-X)) plt.plot(X,y,b-) from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model.logistic import LogisticRegression from sklearn.cross_validation import train_test_split pandas.csvtrain_test_split75%25% X_train_raw, X_test_raw, y_train, y_test = train_test_split(df[1],df[0]) TfidfVectorizerTF-IDF vectorizer=TfidfVectorizer() X_train=vectorizer.fit_transform(X_train_raw) X_test=vectorizer.transform(X_test_raw) LogisticRegressionfit()predict() classifier=LogisticRegression() classifier.fit(X_train,y_train) predictions=classifier.predict(X_test) for i ,prediction in enumerate(predictions[-5:]): print […]

Read More → Python_sklearnlogistic regression## scikit-learn Tutorials

An introduction to machine learning with scikit-learn Machine learning: the problem setting A tutorial on statistical-learning for scientific data processing Statistical learning: the setting and the estimator object in scikit-learn Supervised learning: predicting an output variable from high-dimensional observations Model selection: choosing estimators and their parameters Unsupervised learning: seeking representations of the data Extracting features […]

Read More → scikit-learn Tutorials## Skflow Sklearn-Like Interface for TensorFlow for Deep Learning

This is a simplified interface for TensorFlow, to get people started on predictive analytics and data mining. Library covers variety of needs from linear models toDeep Learningapplications like text and image understanding. TensorFlow provides a good backbone for building different shapes of machine learning applications. It will continue to evolve both in the distributed direction […]

Read More → Skflow Sklearn-Like Interface for TensorFlow for Deep Learning## sklearn-ѧϰDimensionalityreductionά-featureselectionѡ

ǰã sklearn-ѧϰDimensionalityreductionά-featureselectionѡ sklearn-ѧϰDimensionalityreductionά-featureselectionѡ sdkʱԷǳΪҪʹöӦǩܵsdkɹʹAndroidStudioGradleܼ֮Ľ⡣1.ǰǩļŵ̸Ŀ¼Ϊ˱ͳһ2.Gradle룺//keystoreǩsigningConfigsrelease ҪԶӦĵݽԴʾΪ feature selection ã ӷscore ڸݼϵı 1.13.1. Removing features with low variance from sklearn.feature_selection import VarianceThreshold X = [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]] sel = VarianceThreshold(threshold=(.8 * (1 – .8))) sel.fit_transform(X) array([[0, 1], [1, 0], [0, 0], [1, […]

Read More → sklearn-ѧϰDimensionalityreductionά-featureselectionѡ## Linear regression in python scikit learn

In this section, I am going to explain how to use scikit learn /sk learn(a machine learning package in python) to do Linear regression for a set of data points. Please go through the previous section – Linear Regression theory for better understanding. I am not going to explain training-testing data, model evaluation concepts here, […]

Read More → Linear regression in python scikit learn## Linear Regression in Python using scikit-learn

In this post, well be exploring Linear Regression using scikit-learn in python. We will use the physical attributes of a car to predict its miles per gallon (mpg). Linear regression produces a model in the form: $ Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_n X_n $ The way this is accomplished […]

Read More → Linear Regression in Python using scikit-learn## MyJourneyAsaDataScientist

About eighteen months ago I decided to leave astronomy, change my career trajectory and follow the Data Science Bandwagon- this is a blog about that ongoing journey becoming a Data Scientist EVEN more resources online Lessons in Linear regression- Analytics Edge in python: Week 2 DataScience,edX,linear regression,Python,sklearn,Uncategorized Analytic Edge,data science,multicollinearity,pandas,Python,quality of regression model,statsmodels As I […]

Read More → MyJourneyAsaDataScientist## Example of logistic regression in Python using scikit-learn

Back in April, I provided a worked example of areal-world linear regression problem using R. These types of examples can be useful for students getting started in machine learning because they demonstrate both the machine learning workflow and the detailed commands used to execute that workflow. This time around, I wanted to provide a machine […]

Read More → Example of logistic regression in Python using scikit-learn## Business Analytics

Interactive Data Stories with D3.js Interactive Data Stories with D3.js Archive by Category Business Analytics Introduction to Altair A Declarative Visualization Library in Python Introduction Visualization is one of the most exciting parts of data science. Plotting huge amounts of data to unveil underlying relationships has its own Analytics and Data Science is a fast […]

Read More → Business Analytics## 11 Generalized Linear Models

1.1.1.1. Ordinary Least Squares Complexity 1.1.2.2. Setting the regularization parameter: generalized Cross-Validation 1.1.3.1. Setting regularization parameter 1.1.3.1.2. Information-criteria based model selection 1.1.3.1.3. Comparison with the regularization parameter of SVM 1.1.9. Orthogonal Matching Pursuit (OMP) 1.1.10.1. Bayesian Ridge Regression 1.1.10.2. Automatic Relevance Determination – ARD 1.1.12. Stochastic Gradient Descent – SGD 1.1.14. Passive Aggressive Algorithms 1.1.15. […]

Read More → 11 Generalized Linear Models## Selecting good features Part II linear models and regularization

In my previous post I discussedunivariate feature selectionwhere each feature is evaluated independently with respect to the response variable. Another popular approach is to utilize machine learning models for feature ranking. Many machine learning models have either some inherent internal ranking of features or it is easy to generate the ranking from the structure of […]

Read More → Selecting good features Part II linear models and regularization## Selecting good features Part IV stability selection RFE and everything side by side

A blog on machine learning, data mining and visualization Selecting good features Part IV: stability selection, RFE and everything side by side In my previous posts, I looked atunivariate methodslinear models and regularizationandrandom forestsfor feature selection. In this post, Ill look at two other methods: stability selection and recursive feature elimination (RFE), which can both […]

Read More → Selecting good features Part IV stability selection RFE and everything side by side## sklearn(Logistic RegressionLR)

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) scikit-learn3LogisticRegression LogisticRegressionCV logistic_regression_pathLogisticRegressionLogisticRegressionCVLogisticRegressionCVCLogisticRegressionC LogisticRegressionLogisticRegressionCV logistic_regression_pathlogistic_regression_path scikit-learnRandomizedLogisticRegression,L1 LogisticRegressionLogisticRegressionCV LogisticRegressionLogisticRegressionCVpenaltyl1l2.L1L2L2 penaltyL2L2L1 penaltysolverL24newton-cg, lbfgs, liblinear, sagpenaltyL1liblinearL1newton-cg, lbfgs,sagliblinear solver4 a) liblinearliblinear b) lbfgs c) newton-cg d) sagSAGSGDSAG SAGSVRG newton-cg, lbfgssagL1L2liblinearL1L2 sag10sagsagL1L1L2 In a nutshell, one may choose the solver with the following rules: newton-cg, […]

Read More → sklearn(Logistic RegressionLR)## Source code for sklearnmetricsregression

Statistical Analysis and Data Exploration Metrics to assess performance on regression task Functions named as “*_score“ return a scalar value to maximize: the higher Function named as “*_error“ or “*_loss“ return a scalar value to minimize: Authors: Alexandre Gramfort alexandre. Michael Eickenberg michael. Konstantin Shmelkov konstantin. explained_variance_score Check that y_true and y_pred belong to the […]

Read More → Source code for sklearnmetricsregression## Linear Regression Using Scikit-Learn

Want to learn machine learning? Use mymachine learning flashcards. Linear Regression Using Scikit-Learn array([ -1.07170557e-01, 4.63952195e-02, 2.08602395e-02, 2.68856140e+00, -1.77957587e+01, 3.80475246e+00, 7.51061703e-04, -1.47575880e+00, 3.05655038e-01, -1.23293463e-02, -9.53463555e-01, 9.39251272e-03, -5.25466633e-01]) Everything on this site is available on GitHub. Head toand submit a suggested change. You can also message me directly onTwitter.

Read More → Linear Regression Using Scikit-Learn## Linear Regression Using Python scikit-learn

1024 && edition) class=content-right-images ntent 1 && !articles[0].partner.isSponsoringArticle dc-slot=ads.sb2.slot(articles[0], 0) tags=ads.sb2.tags(articles[0], 0) size=ads.sb2.size(articles[0], 0) Linear Regression Using Python scikit-learn Linear Regression Using Python scikit-learn Lets say you have some peoples height and weight data. Can you use it to predict other peoples weight? Find out using Python scikit-learn. Join the DZone community and get the […]

Read More → Linear Regression Using Python scikit-learn## Supervised Learning with scikit-learn

This course is part of these tracks: Core developer of scikit-learn; Lecturer at Columbia University Andy is a lecturer at the Data Science Institute at Columbia University and author of the OReilly book Introduction to machine learning with Python, describing a practical approach to machine learning with python and scikit-learn. He is one of the […]

Read More → Supervised Learning with scikit-learn## How to do a linear regression with sklearn

Wed Dec 21 2016 00:00:00 GMT+0000 (UTC) We require the user to have apython anaconda environmentalready installed. Test that scikit-learn was correctly installed:: We are going to choose fixed values of m and b for the formula y = x*m + b. Then with a random error of 1% will generate the random points. Usually […]

Read More → How to do a linear regression with sklearn