scikit-learn Cookbook 附带源代码
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2017-07-19
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scikit-learn Cookbook 附带源代码 （195个子文件） 
._chapter2 222B ._chapter3 222B .DS_Store 6KB ._.DS_Store 120B TuningADecisionTree.ipynb 1.47MB Support Vector Machines.ipynb 272KB Blurring an Image.ipynb 189KB 978-1-78398-948-5_2_10.ipynb 181KB Assessing Cluster Correctness.ipynb 154KB Assessing Cluster Correctness-checkpoint.ipynb 154KB Guassian Processes.ipynb 138KB Clustered Regression-checkpoint.ipynb 137KB Clustered Regression.ipynb 137KB Using KMeans to Cluster Data.ipynb 126KB Using KMeans to Cluster Data-checkpoint.ipynb 126KB Outlier Detection-checkpoint.ipynb 102KB Outlier Detection.ipynb 102KB GMM.ipynb 81KB GMM-checkpoint.ipynb 81KB DecisionTrees.ipynb 70KB Dictionary Learning.ipynb 69KB Dictionary Learning-checkpoint.ipynb 69KB KPCA.ipynb 62KB Dirichlet Process Gaussian Mixture Model - 2.ipynb 60KB 978-1-78398-948-5_2_6-checkpoint.ipynb 59KB 978-1-78398-948-5_2_6.ipynb 59KB Using LDA for Classification-checkpoint.ipynb 58KB Using LDA for Classification.ipynb 58KB 978-1-78398-948-5_2_8-checkpoint.ipynb 51KB 978-1-78398-948-5_2_8.ipynb 51KB 978-1-78398-948-5_2_2.ipynb 46KB 978-1-78398-948-5_2_2-checkpoint.ipynb 46KB 978-1-78398-948-5_2_9-checkpoint.ipynb 42KB 978-1-78398-948-5_2_9.ipynb 42KB Regression_Model_Evaluation.ipynb 41KB Tuning a Random Forest Model.ipynb 40KB ShuffeSplit.ipynb 40KB Finding the Closest Objects in the Feature Space-checkpoint.ipynb 39KB Finding the Closest Objects in the Feature Space.ipynb 39KB stratifiedkfold.ipynb 39KB 978-1-78398-948-5_2_3.ipynb 35KB 978-1-78398-948-5_2_3-checkpoint.ipynb 35KB TruncatedSVD.ipynb 35KB TruncatedSVD-checkpoint.ipynb 35KB Using Many Decisions Trees - Random Forests.ipynb 34KB 978-1-78398-948-5_2_1.ipynb 33KB 978-1-78398-948-5_2_4.ipynb 27KB 978-1-78398-948-5_2_4-checkpoint.ipynb 27KB Optimizing the Number of Centroids.ipynb 26KB Optimizing the Number of Centroids-checkpoint.ipynb 26KB 978-1-78398-948-5_2_7.ipynb 23KB 978-1-78398-948-5_2_7-checkpoint.ipynb 23KB poormans_search.ipynb 16KB FeatureSelection.ipynb 15KB FeatureSelectiononL1Norms.ipynb 12KB 978-1-78398-948-5_2_5-checkpoint.ipynb 10KB 978-1-78398-948-5_2_5.ipynb 10KB Classifying Documents with Naïve Bayes.ipynb 9KB Brute_Force_Grid_Search.ipynb 9KB K-fold_Cross_Validation.ipynb 9KB K-fold_Cross_Validation-checkpoint.ipynb 9KB Dummy_Estimators.ipynb 9KB Using Minibatch KMeans.ipynb 8KB Put toget with pipelines.ipynb 8KB Using Pipelines for multiple preprocessing steps-checkpoint.ipynb 7KB Using Pipelines for multiple preprocessing steps.ipynb 7KB Binarizing label features-checkpoint.ipynb 7KB Binarizing label features.ipynb 7KB Imputing missing values through various strategies.ipynb 7KB Imputing missing values through various strategies-checkpoint.ipynb 7KB Persisting_Models_with_Joblib.ipynb 6KB multiclass_classification-checkpoint.ipynb 6KB multiclass_classification.ipynb 6KB Scaling data to the standard normal-checkpoint.ipynb 6KB Scaling data to the standard normal.ipynb 6KB AutomaticCrossValidation.ipynb 6KB Working with categorical variables-checkpoint.ipynb 6KB Working with categorical variables.ipynb 6KB Getting sample data from external sources.ipynb 5KB Getting sample data from external sources-checkpoint.ipynb 5KB Creating binary features through thresholding.ipynb 5KB Creating binary features through thresholding-checkpoint.ipynb 5KB Propagating Labels with Semi Supervised Learning-checkpoint.ipynb 4KB Propagating Labels with Semi Supervised Learning.ipynb 4KB Classification_Model_Evaluation.ipynb 4KB Using Stochastic Gradient Descent for Regression.ipynb 3KB Using Stochastic Gradient Descent for Regression-checkpoint.ipynb 3KB Reducing dimensionality with PCA-checkpoint.ipynb 3KB Reducing dimensionality with PCA.ipynb 3KB Creating sample data for toy analysis-checkpoint.ipynb 3KB Creating sample data for toy analysis.ipynb 3KB Defining the Guassian Process object directly.ipynb 2KB Defining the Guassian Process object directly-checkpoint.ipynb 2KB Kernel PCA for nonlinear dimensionality reduction-checkpoint.ipynb 2KB Kernel PCA for nonlinear dimensionality reduction.ipynb 2KB Using factor analysis for decomposition.ipynb 2KB Using factor analysis for decomposition-checkpoint.ipynb 2KB Using Stochastic Gradient Descent for Classification.ipynb 1KB Using Stochastic Gradient Descent for Classification-checkpoint.ipynb 1KB ._978-1-78398-948-5_2_3.ipynb 222B共 195 条
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yinshuwei2018-04-12果然是源代码
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