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文档列表

06 Wrapper methods/033 Step backward feature selection.mp4 33.6 MB
06 Wrapper methods/032 Step forward feature selection.mp4 31.0 MB
04 Filter methods Correlation/022 Correlation.mp4 25.6 MB
03 Filter Methods Basics/019 Duplicated features.mp4 21.7 MB
08 Embedded methods Linear models/039 Selection by Logistic Regression Coefficients.mp4 21.1 MB
06 Wrapper methods/034 Exhaustive search.mp4 19.6 MB
05 Filter methods Statistical measures/024 Statistical methods Intro.mp4 17.4 MB
05 Filter methods Statistical measures/027 Univariate approaches.mp4 17.2 MB
02 Feature Selection/012 Feature selection methods Overview.mp4 16.3 MB
06 Wrapper methods/031 Wrapper methods Intro.mp4 16.3 MB
03 Filter Methods Basics/018 Quasi-constant features.mp4 16.1 MB
03 Filter Methods Basics/017 Constant features.mp4 15.2 MB
05 Filter methods Statistical measures/025 Mutual information.mp4 14.7 MB
04 Filter methods Correlation/021 Correlation Intro.mp4 14.6 MB
07 Embedded methods Lasso regularisation/036 Lasso.mp4 14.6 MB
05 Filter methods Statistical measures/028 Univariate ROC-AUC.mp4 11.4 MB
02 Feature Selection/015 Embedded Methods.mp4 10.0 MB
09 Embedded methods Trees/043 Selecting Features by Tree importance Intro.mp4 9.7 MB
03 Filter Methods Basics/016 Constant quasi constant and duplicated features Intro.mp4 9.3 MB
08 Embedded methods Linear models/040 Coefficients change with penalty.mp4 8.9 MB
07 Embedded methods Lasso regularisation/035 Regularisation Intro.mp4 8.3 MB
02 Feature Selection/011 What is feature selection.mp4 8.2 MB
02 Feature Selection/014 Wrapper methods.mp4 7.7 MB
05 Filter methods Statistical measures/026 Chi-square for categorical variables Fisher score.mp4 7.6 MB
01 Introduction/003 Course requirements.mp4 6.7 MB
01 Introduction/008 Feature-selection-presentations.zip 6.3 MB
08 Embedded methods Linear models/038 Regression Coefficients Intro.mp4 5.7 MB
08 Embedded methods Linear models/041 Selection by Linear Regression Coefficients.mp4 5.3 MB
02 Feature Selection/013 Filter Methods.mp4 5.1 MB
01 Introduction/001 Introduction.mp4 4.8 MB
01 Introduction/002 Course Curriculum Overview.mp4 4.3 MB
01 Introduction/009 Feature-selection-notebooks.zip 937.1 kB
Discuss.FreeTutorials.Us.html 169.7 kB
FreeCoursesOnline.Me.html 110.9 kB
FreeTutorials.Eu.html 104.7 kB
11 Hybrid feature selection methods/051 BONUS Hybrid method Recursive feature addition.html 52.3 kB
11 Hybrid feature selection methods/050 BONUS Hybrid method Recursive feature elimination.html 50.0 kB
11 Hybrid feature selection methods/049 BONUS Shuffling features.html 20.5 kB
05 Filter methods Statistical measures/030 BONUS select features by mean encoding KDD 2009.html 19.7 kB
04 Filter methods Correlation/022 Correlation-en.srt 19.1 kB
07 Embedded methods Lasso regularisation/037 Basic filter methods LASSO pipeline.html 16.5 kB
09 Embedded methods Trees/047 Feature selection with decision trees review.html 16.1 kB
08 Embedded methods Linear models/042 Feature selection with linear models review.html 15.9 kB
05 Filter methods Statistical measures/024 Statistical methods Intro-en.srt 15.8 kB
09 Embedded methods Trees/044 Select by model importance random forests embedded.html 15.5 kB
06 Wrapper methods/032 Step forward feature selection-en.srt 14.8 kB
06 Wrapper methods/033 Step backward feature selection-en.srt 14.8 kB
05 Filter methods Statistical measures/029 Basic methods Correlation univariate ROC-AUC pipeline.html 14.4 kB
03 Filter Methods Basics/017 Constant features-en.srt 13.1 kB
03 Filter Methods Basics/018 Quasi-constant features-en.srt 12.8 kB
05 Filter methods Statistical measures/027 Univariate approaches-en.srt 12.5 kB
04 Filter methods Correlation/023 Basic methods plus Correlation pipeline.html 11.4 kB
09 Embedded methods Trees/045 Select by model importance random forests recursively.html 11.4 kB
07 Embedded methods Lasso regularisation/036 Lasso-en.srt 10.6 kB
06 Wrapper methods/034 Exhaustive search-en.srt 10.5 kB
05 Filter methods Statistical measures/025 Mutual information-en.srt 10.2 kB
09 Embedded methods Trees/046 Select by model importance gradient boosted machines.html 9.9 kB
08 Embedded methods Linear models/039 Selection by Logistic Regression Coefficients-en.srt 9.8 kB
05 Filter methods Statistical measures/028 Univariate ROC-AUC-en.srt 9.0 kB
03 Filter Methods Basics/019 Duplicated features-en.srt 8.8 kB
06 Wrapper methods/031 Wrapper methods Intro-en.srt 8.6 kB
09 Embedded methods Trees/043 Selecting Features by Tree importance Intro-en.srt 8.4 kB
02 Feature Selection/011 What is feature selection-en.srt 7.6 kB
02 Feature Selection/012 Feature selection methods Overview-en.srt 7.5 kB
07 Embedded methods Lasso regularisation/035 Regularisation Intro-en.srt 6.9 kB
08 Embedded methods Linear models/040 Coefficients change with penalty-en.srt 6.9 kB
04 Filter methods Correlation/021 Correlation Intro-en.srt 6.8 kB
02 Feature Selection/014 Wrapper methods-en.srt 6.5 kB
05 Filter methods Statistical measures/026 Chi-square for categorical variables Fisher score-en.srt 5.7 kB
01 Introduction/001 Introduction-en.srt 5.6 kB
08 Embedded methods Linear models/038 Regression Coefficients Intro-en.srt 5.3 kB
03 Filter Methods Basics/016 Constant quasi constant and duplicated features Intro-en.srt 5.1 kB
02 Feature Selection/015 Embedded Methods-en.srt 5.1 kB
01 Introduction/002 Course Curriculum Overview-en.srt 5.0 kB
03 Filter Methods Basics/020 Basic methods review.html 4.7 kB
01 Introduction/003 Course requirements-en.srt 4.5 kB
01 Introduction/006 Guide to setting up your computer.html 4.2 kB
08 Embedded methods Linear models/041 Selection by Linear Regression Coefficients-en.srt 4.0 kB
02 Feature Selection/013 Filter Methods-en.srt 4.0 kB
01 Introduction/007 Installing XGBoost in windows.html 3.0 kB
10 Reading Resources/048 Additional reading resources.html 2.6 kB
01 Introduction/005 How to approach this course.html 2.4 kB
01 Introduction/010 FAQ Data Science and Python programming.html 1.9 kB
01 Introduction/004 Additional Requirements Nice to have.html 1.5 kB
12 Final section Next steps/052 Bonus Lecture Discounts on my other courses.html 1.4 kB
01 Introduction/008 Presentations covered in this course.html 994 Bytes
01 Introduction/009 Jupyter notebooks covered in this course.html 994 Bytes
[TGx]Downloaded from torrentgalaxy.org.txt 524 Bytes
Torrent Downloaded From GloDls.to.txt 84 Bytes
07 Embedded methods Lasso regularisation/035 Machine-Learning-Explained-Regularization.txt 71 Bytes
07 Embedded methods Lasso regularisation/035 Least-angle-and-1-penalized-regression-A-review-.txt 68 Bytes
Presented By SaM.txt 33 Bytes

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