🌐 Mailman 3 August 2018 - scikit-learn - python.org

Small suggestion for documentation
by Fellype Aug. 7, 2018

Aug. 7, 2018

Dear maintainers,I've just known scikit-learn and found it very useful. Congratulations for this library. I found some confuse terms to describe r2_score parameters in documentation [1]. For me, the meanings of y_true and y_pred are not clear. From [1]:- y_true: ... Ground truth (correct) target values- y_pred: ... Estimated target values Since the R^2 value is usually used to compare the behavior of experimental data (observed) with a theoretical model or standard data (expected), I guess that it would be better to change the description of y_true and y_pred to something like:- y_true: ... Observed (or measured) target values- y_pred: ... Expected (or theoretical) target values I also think that the same should be done in documentation of other scikit-learn functions that use the y_true and y_pred terms with the same meaning. Thanks for your attention and best wishes. Fellype [1] http://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.h…

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RidgeCV with multiple targets returns a single alpha. Is it possible to get one alpha per target?
by Christophe Pallier Aug. 7, 2018

Aug. 7, 2018

Hello, I'd like to use RidgeCV to find the optimal alpha for each colunm (ntargets) of the DV variable. It lloks like itthe fit() computes a single alpha. Is there a way to compute one alpha per column? -- -- Christophe Pallier <christophe(a)pallier.org> INSERM-CEA Cognitive Neuroimaging Lab, Neurospin, bat 145, 91191 Gif-sur-Yvette Cedex, France Tel: 00 33 1 69 08 79 34 Personal web site: http://www.pallier.org Lab web site: http://www.unicog.org

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Normalize an image with 3 channels
by Prathusha Jonnagaddla Subramanyam Naidu Aug. 7, 2018

Aug. 7, 2018

Hi everyone, I'm trying to extract features from images using VGG and I have to normalize each image array before doing that. Each array is of the size (224, 224 ,3) and I'm unable to use the MinMaxScalar in this case. Would appreciate any help with this . Thank you ! -- Regards, Prathusha JS Naidu

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Random forest classification source code
by hoang trung Ta Aug. 7, 2018

Aug. 7, 2018

Dear all members, I am running Random forest for my research using Scikit learn. I want to see the source code of the random forest in Scikit learn to understand how it works, thus *Can you tell me where I can find this source code, please ?* Thank you very much -- *Ta Hoang Trung (Mr)* *Master student* Graduate School of Life and Environmental Sciences University of Tsukuba, Japan Mobile: +81 70 3846 2993 Email : ta.hoang-trung.xm(a)alumni.tsukuba.ac.jp tahoangtrung(a)gmail.com s1626066(a)u.tsukuba.ac.jp *----* *Mapping Technician* Department of Surveying and Mapping Vietnam No 2, Dang Thuy Tram street, Hanoi, Viet Nam Mobile: +84 1255151344 Email : tahoangtrung(a)gmail.com

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My first Program using sklearn
by Thouraya TH Aug. 5, 2018

Aug. 5, 2018

Hi, Please i'd like to use prediction techniques in tenserflow. I have this file: x | y 1| 1 2| 4 4|16 --> prediction techniques must give me this model y=x*x using this model i can predict the value y of x=3 This is a sample example. In my experiment i use a file with 1000 lines. Please, how can transform these lines on codes ? Code: from sklearn.preprocessing import PolynomialFeatures from sklearn import linear_model X = [[1, 2, 4], [1, 4, 16]] poly = PolynomialFeatures(degree=2) X_ = poly.fit_transform(X) Thank you so much for help. Kind regards.

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pipeline for modifying target and number of samples
by David Burns Aug. 4, 2018

Aug. 4, 2018

Hi, I posted a while back about this, and am reposting now since I have made progress on this topic. As you are probably aware, the sklearn Pipeline only supports transformers for X, and the number of samples must stay the same. I work with time series where the learning pipeline relies on transformations like resampling, segmentation, etc that change the target and number of samples in the data set. In order to address this, I created an sklearn compatible pipeline that handles transformers that alter X, y, and sample_weight together. It can undergo model selection using the sklearn tools, and integrates with all the sklearn transformers and estimators. It also has some new options for setting hyper-parameters with callables and in reference to other parameters. The implementation is in my time series package seglearn: https://github.com/dmbee/seglearn - Best David Burns

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One-hot encoding
by Sarah Wait Zaranek Aug. 3, 2018

Aug. 3, 2018

Hello - I was just wondering if there was a way to improve performance on the one-hot encoder. Or, is there any plans to do so in the future? I am working with a matrix that will ultimately have 20 million categorical variables, and my bottleneck is the one-hot encoder. Let me know if this isn't the place to inquire. My code is very simple when using the encoder, but I cut and pasted it here for completeness. enc = OneHotEncoder(sparse=True) Xtrain = enc.fit_transform(tiledata) Thanks, Sarah

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Query about an algorithm
by Shantanu Bhattacharya Aug. 1, 2018

Aug. 1, 2018

Hello, I am new to this mailing list. I would like to understand the algorithms provided. Is second order gradient descent with hessian error matrix supported by this library? I went through the documentation, but did not find it. Are you able to confirm or direct me to some place that might have it? Look forward to your thoughts Kind regardsShantanu

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