In the PCA section there is the following quote:

We see that these 150 components account for just over 90% of the variance.

While not inaccurate (150 componen

Alexnet implementation in tensorflow has incomplete architecture where 2 convolution neural layers are missing. This issue is in reference to the python notebook mentioned below.

https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb

Several parts of the op sec like the main op description, attributes, input and output descriptions become part of the binary that consumes ONNX e.g. onnxruntime causing an increase in its size due to strings that take no part in the execution of the model or its verification.

Setting __ONNX_NO_DOC_STRINGS doesn't really help here since (1) it's not used in the SetDoc(string) overload (s

Hi,
I'm new to tpot but I got this error. I understand that score function can take strings, but I got the following error when using TPOTClassifier.

---

ValueError Traceback (most recent call last)
in
----> 1 tpot.score(X_test, y_test)

~/miniconda3/envs/ml

If you join Dask DataFrame on a categorical column, then the outputted Dask DataFrame column is still category dtype. However, the moment you .compute() the outputted Dask DataFrame, then the column is the wrong dtype, not categorical.

Tested on Dask 2.14.0 and Pandas 1.0.3
This example where the category type looks like a float, so after .compute(), the dtype is float.

import dask.d

(p380) In last sentence of 1st paragraph and first sentence of 2nd paragraph, "Pitt" should be "Pitts".

(p384) In a picture, "3nd Layer" should be "3rd Layer".

(p385) In 1st paragraph, "the o superscript" should be "the out superscript".

(p406) In 1st code block, "print('Training accuracy: ..)" should be "print('Test accuracy: ..)"

(p410) In 1st paragraph,

For example, if there is a relationship transaction.session_id -> sessions.id and we are calculating a feature transactions: sessions.SUM(transactions.value) any rows for which there is no corresponding session should be given the default value of 0 instead of NaN.

Of course this should not normally occur, but when it does it seems more reasonable to use the default_value.

`DirectF

i want to know whether autosklearn support multilabel task, thank you!

  File "/root/miniconda3/bin/pipeline", line 11, in <module>
    sys.exit(_main())
  File "/root/miniconda3/lib/python3.7/site-packages/cli_pipeline/cli_pipeline.py", line 5734, in _main
    _fire.Fire()
  File "/root/miniconda3/lib/python3.7/site-packages/fire/core.py", line 127, in Fire
    component_trace = _Fire(component, args, context, name)
  Fil

I think it would be good if we apply the GitHub badge named donate rather than the huge QR codes of Alipay and WeChat Pay. It's not elegant. We can generate badges at https://shields.io/#/

I see the code

device = ‘cuda’ if torch.cuda.is_available() else ‘cpu’

repeated often in user code. Maybe we should introduce device='auto' exactly for this case?

On this page at ClassificationReport

For binary classifier, the part “not to label an instance positive that is actually negative” sounded like “to correctly classif

Would you write some docstrings for the models? Docstrings and explanations for the model parameters would make it easier to understand and use them.

Most of the people who start out new don't find a latest feed of community hyped resources on ML and DL topics. It would be pretty good if we add a Reddit section.
If you're fine with this suggestion I'll put up a PR with the update 😄

I tried building the docs, but was met with a graphviz error. Typically this means I can spend a few hours pecking away at the dependencies until I get stable build... or someone that has it working can export their environment, and publish an environment.yml that we can use with the build instructions.
I was going off of the d2l book since that's a dep here, but their [environment.yml](https://g

RFE/RFECV are not only feature selectors (SelectorMixin) but also classifiers/regressors (MetaEstimatorMixin), though ELI5 explain_weights doesn't support them as classifiers/regressors. The final fit of an RFE/RFECV object is a fitted estimator with either rfe.estimator_.coef_ or rfe.estimator_.feature_importances_ and in sklearn you do not usually follow up RFE/RFECV with another classifier

I think it could be useful, when one wants to plot only e.g. class 1, to have an option to produce consistent plots for both plot_cumulative_gain and plot_roc

At the moment, instead, only plot_roc supports such option.

Thanks a lot

Support error function and fresnel integrals in https://docs.scipy.org/doc/scipy/reference/special.html#error-function-and-fresnel-integrals, those are not universal functions may not need to be supported.

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.