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andrewji8

Being towards death

Heed not to the tree-rustling and leaf-lashing rain, Why not stroll along, whistle and sing under its rein. Lighter and better suited than horses are straw sandals and a bamboo staff, Who's afraid? A palm-leaf plaited cape provides enough to misty weather in life sustain. A thorny spring breeze sobers up the spirit, I feel a slight chill, The setting sun over the mountain offers greetings still. Looking back over the bleak passage survived, The return in time Shall not be affected by windswept rain or shine.
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Autogluon, a magical python library.

Today I'm going to share a powerful Python library called autogluon.

https://github.com/autogluon/autogluon
AutoGluon is an AutoML toolkit for deep learning that can automatically perform end-to-end machine learning tasks, allowing us to achieve powerful predictive performance with just a few lines of code.
AutoGluon "automates machine learning tasks" and allows you to easily achieve powerful predictive performance in your applications.
First experience
Installing AutoGluon
We can directly install it using pip.

pip install autogluon

Loading datasets
We can load datasets using TabularDataset.

from autogluon.tabular import TabularDataset, TabularPredictor
train_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
test_data = TabularDataset('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')

Model building
To use the model, we need to initialize "evaluation metrics, dependent variables, and the directory to store the results."

In the following example, we use f1 as the evaluation metric. The dependent variable is "class", and the models are stored in the "output_models" folder.

evaluation_metric = "f1"
data_label = "class"
save_path = "output_models"
predictor = TabularPredictor(label='class').fit(train_data, time_limit=120)  # Fit models for 120s
leaderboard = predictor.leaderboard(test_data)
# Create predictor
predictor = TabularPredictor(label=data_label, path=save_path, eval_metric=evaluation_metric) 
predictor = predictor.fit(train_data)
predictor.leaderboard(silent=True)

The leaderboard shown in the image below allows you to see "the attempts made with all models and the scores you obtained with these models."

image
Now let's take a look at feature importance.

X = train_data 
Predictor.feature_importance(X)

image

All built models are stored in the output folder "output_models".

image

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