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.
0.22.0 because we corrected the bug in 0.22.1
import pandas as pd
import sklearn
import numpy as np
raw_data = {'Binary 1': [True, True, False, False, True],
'Binary 2': [False, False, True, True, False],
'age': [42, 52, 36, 24, 73],
'preTestScore': [4.4, 24.1, 31.3, 2.2, 3.1],
'postTestScore': [25.7, 94.5, 57.0, 62.2, 70.9]}
df = pd.DataFrame(raw_data, columns = ['Binary 1', 'Binary 2', 'age', 'preTestScore', 'postTestScore'])
X_train = df[['Binary 1', 'Binary 2', 'age', 'preTestScore']]
y_train = df['postTestScore']
print(X_train.dtypes)
X, y = sklearn.utils.check_X_y(X_train, y_train, dtype=[np.float64], force_all_finite=False)
print(X.dtype)
X, y = sklearn.utils.check_X_y(X_train.values, y_train.values, dtype=[np.float64], force_all_finite=False)
print(X.dtype)
X_train = df[['Binary 1', 'Binary 2', 'age']]
y_train = df['postTestScore']
X, y = sklearn.utils.check_X_y(X_train, y_train, dtype=[np.float64], force_all_finite=False)
print(X.dtype)
X, y = sklearn.utils.check_X_y(X_train.values, y_train.values, dtype=[np.float64], force_all_finite=False)
print(X.dtype)
Curious To Learn & Contribute To Scikit-learn At The 'Paris Scikit-learn Sprint Of The Decade' | Jan 28 - 31, 2020
It was quite insightful listening to Reshama Shaikh's recent podcast: https://www.listennotes.com/podcasts/the-banana-data/bdn-15-finding-community-in-uK-yL2tf_S4/
It was quite helpful to broaden my horizon and perspective on open-source when I learned the challenges the organization faces in finding the sponsors and fundraisers for scikit-learn sprint events.
As an avid user of scikit-learn for my research projects in the recent past, I’m excited about the potential of contributing and working alongside other attendees at the Paris scikit-learn sprint. Reshama's comments about funding & accessibility have made me even more eager to join the team.
Would you all mind letting me know if I could connect with the other participants remotely from Bengaluru, India?
Best,
Sandeep Aswathnarayana
Hey, I opened the PR to add Neuraxle to the Related Projects page:
scikit-learn/scikit-learn#16100
I've put it under the category for Auto-ML as it seems better suited here. I'm still developing the serialization plugin/extra libraries for TensorFlow and PyTorch as of right now, so those plugins could go into the Model export for production category later on (to allow saving / reloading / then continue training / partial_fit whenever after).
pytest sklearn/metrics/_classification.py
ImportError while loading conftest '/media/sid21g/Dev/github-dev/scikit-learn/conftest.py'.
conftest.py:15: in <module>
from sklearn import set_config
sklearn/__init__.py:81: in <module>
from . import __check_build # noqa: F401
sklearn/__check_build/__init__.py:46: in <module>
raise_build_error(e)
sklearn/__check_build/__init__.py:41: in raise_build_error
%s""" % (e, local_dir, ''.join(dir_content).strip(), msg))
E ImportError: No module named 'sklearn.__check_build._check_build'
E ___________________________________________________________________________
E Contents of /media/sid21g/Dev/github-dev/scikit-learn/sklearn/__check_build:
E setup.py _check_build.c _check_build.pyx
E __init__.py __pycache__
E ___________________________________________________________________________
E It seems that scikit-learn has not been built correctly.
E
E If you have installed scikit-learn from source, please do not forget
E to build the package before using it: run `python setup.py install` or
E `make` in the source directory.
E
E If you have used an installer, please check that it is suited for your
E Python version, your operating system and your platform.
no one answered me There, but there is a desire to understand.
I apologize in advance for my poor English).
@quant12345 as already replied to your post, have a read to this: https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py
In RF, feature importance is affected by features able to overfit the model.
In a vanilla implementation of gradient boosting (ignoring the sub-estimator which is a tree in our case), the only parameters are the learning rate / shrinkage, the loss, and the number of iterations.
