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automl's Introduction

Checking possibilities of AutoML package from MLJAR

based on the https://github.com/mljar/mljar-supervised

If someone is looking for ready to use, easy to install( here on lubuntu) automatised ml package for analysis of tabular data this is really excellent choice.
I am really impressed by way of presenting and storage results by mentioned package.
To check and play input data with Moskow flat prices were used.
Typical example for regresion to evaluate the price metric mae.

mljar.ipynb

Below there is starting screen of leaderboard with reasults generated by package:

### leaderboard

AutoML Leaderboard

Best model name model_type metric_type metric_value train_time
1_Baseline Baseline mae 0.225592 2.68
2_DecisionTree Decision Tree mae 0.186864 21.44
the best 3_Default_Xgboost Xgboost mae 0.111216 29.3
4_Default_NeuralNetwork Neural Network mae 0.136117 9.68
5_Default_RandomForest Random Forest mae 0.16243 39.82
Ensemble Ensemble mae 0.111216 1.53

AutoML Performance

AutoML Performance

AutoML Performance Boxplot

AutoML Performance Boxplot

Features Importance

features importance across models

Spearman Correlation of Models

models spearman correlation

automl's People

Contributors

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Watchers

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