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While training AutoML on small datasets:
```python
catboost/private/libs/algo/apply.cpp:42: Out of range treeEnd=155
Traceback (most recent call last):
File "/home/piotr/sandbox/mljar-supervis…
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```python
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from supervised.automl import AutoML # mlj…
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In main `README.md` there should be a link to EDA if it is available.
***Detail desc***:
- in the file `supervised/base_automl.py` there is created main `README.md` for `AutoML` in `select_and_sav…
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Maybe it is a good idea to integrate the parameters tuning part with the Optuna framework. The training for sure will take a long time, but the results might be very good.
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Get an error on both `pip install mljar-supervised` as well as `python setup.py install` after cloning. It might also be nice to add dependencies, I had to install MSVC14.
I'm on Windows 10, Python …
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I have a dataset X_train, features data: pandas.DataFrame column 'store_type_pdist' has dtype 'category', with numerical values like 0, 1, 2;
when running code like:
```python
automl = AutoML(m…
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`PermutationImportance` was enhanced in #208 to limit excessive computation when the number of columns is large:
```py
rows, cols = X_validation.shape
if cols > 5000…
DWgit updated
3 years ago
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There's an error here, based on the code I suggested. It's stopping now with:
```
train_size=10 should be either positive and smaller than the number of samples 10 or a float in the (0, 1) range
Pr…
DWgit updated
3 years ago
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Thank you so much for your great repo. Please tell us how to save and reload models.
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I will use metric from scikit-learn https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html