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scikit-learn/scikit-learn

scikit-learn: machine learning in Python. Please feel free to ask specific questions about scikit-learn. Please try to keep the discussion focused on scikit-learn usage and immediately related open source projects from the Python ecosystem.

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Ghost

@ghost~5bc98094d73408ce4fabf741

Thanks for the answers @amueller and @rahulunair , I wasn't aware you have models for working with imbalanced data sets specifically, how do they fare against large sparse matrices, I am talking word vectors?

Rahul Nair

@rahulunair

@piotr-mamenas I would consider the word vectors as the input embeddings? and if you are looking to classify something, you can look into SVMs that deal with unbalanced classes, essentially it weights the unbalanced class differently.. scikit-learn has a section for that: https://scikit-learn.org/stable/modules/svm.html

or try a tree based algorithms to see how your accuracy numbers and ROC curve is

for accuracy , check out: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html

Ghost

@ghost~5bc98094d73408ce4fabf741

Thanks @rahulunair your response is much appreciated, I will take a look at it.

lesshaste

@lesshaste

if you are doing image classification using scikit learn, do you have to convert the images into 1d arrays first?

Guillaume Lemaitre

@glemaitre

yes

lesshaste

@lesshaste

@glemaitre hmm... that seems to lose some vital information

i..e that pixels next to each other are related

Guillaume Lemaitre

@glemaitre

you can look at an example of load_digits to see that the 8x8 images are transformed to 1d 64 arrays

Olivier Grisel

@ogrisel

you can use a pre-trained convolutional neural network to extract interesting features

or you can use scikit-image HoG features for instance. Depending on the kinds of images, it might be enough.

Guillaume Lemaitre

@glemaitre

you can use a pre-trained convolutional neural network to extract interesting features

which a much better approach

lesshaste

@lesshaste

HoG features?

Histogram of Oriented Gradients ?

Olivier Grisel

@ogrisel

Histogram of Oriented Gradients

yes

Guillaume Lemaitre

@glemaitre

https://scikit-image.org/docs/dev/auto_examples/features_detection/plot_hog.html

lesshaste

@lesshaste

all very interesting thanks. It seems a weakness somehow in the general non-NN classification model that it can't take advantage of 2d data

Guillaume Lemaitre

@glemaitre

I think that I have 2 quick examples showing a bit how things can be connected:

https://scikit-image.org/docs/dev/auto_examples/applications/plot_haar_extraction_selection_classification.html#sphx-glr-auto-examples-applications-plot-haar-extraction-selection-classification-py

https://github.com/scikit-learn/scikit-learn/pull/6509/files

lesshaste

@lesshaste

I suppose even in 1d random forests etc are invariant to permutations of the input array

@glemaitre thanks

Olivier Grisel

@ogrisel

yes, you have to do feature engineering first. You can consider the 2D conv layers before the final flatten / global average pooling as a feature extractor and the last fully connected layers as a standard classifier. It's just that both the feature extraction and the classifier are trained end-to-end together

lesshaste

@lesshaste

it's only really the convolutions that take advantage of the neighborhood of pixels I suppose

@ogrisel right.

Olivier Grisel

@ogrisel

but nowawdays, (convolutional) neural networks are almost always the good solution for image classification, unless you have very specific prior knowledge on the image you want to classify.

lesshaste

@lesshaste

I wonder if random forests could be changed to take arrays of pairs, say, as inputs

@ogrisel that's true but I am also thinking of time series data

where it makes a big difference if two values are from successive times or not

Olivier Grisel

@ogrisel

it's only really the convolutions that take advantage of the neighborhood of pixels I suppose

No: if you have deep conv layers with downsampling (strides or max pooling for instance) the conv layers can capture large high level complex patterns that span a large receptive field.

lesshaste

@lesshaste

@ogrisel you said No but I read your answer as yes :)

Olivier Grisel

@ogrisel

we need an example of some standard feature engineering you can do on time windows for time series forecasting / classification.

lesshaste

@lesshaste

@ogrisel that would be good to see

Olivier Grisel

@ogrisel

I misread the original quote, then yes.

But what I meant is that deep conv net can model non-local patterns

lesshaste

@lesshaste

@ogrisel yes . What I meant is that without any convolutions you don't get to see local patterns

on an NN topic, is there software to give you a good guess at a reasonable architecture for a classification task? I saw autokeras but it's pretty heavy.

Olivier Grisel

@ogrisel

if you really want to use decision trees for image classification you might be interested in https://arxiv.org/abs/1905.10073 but this is not (and will not) be implemented in scikit-learn ;)

lesshaste

@lesshaste

@ogrisel thanks! Why won't it be implemented? Because it doesn't work or coding resources?

Olivier Grisel

@ogrisel

I don't know what is the practical state of the art for architecture search for image classification

lesshaste

@lesshaste

really I am secretly interested in time series

Olivier Grisel

@ogrisel

because it is not a standard, established method.

lesshaste

@lesshaste

@ogrisel got you

image classification was just interesting because the data is in 2d

Olivier Grisel

@ogrisel

https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms

lesshaste

@lesshaste

but even in 1d it seems unclear to me what the right thing to do is

@ogrisel I have read those guidelines! They seem very sensible to me

Olivier Grisel

@ogrisel

https://scikit-learn.org/stable/faq.html#why-is-there-no-support-for-deep-or-reinforcement-learning-will-there-be-support-for-deep-or-reinforcement-learning-in-scikit-learn

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