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Tim
2025-08-18 20:22:59 +02:00
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"``sklearn`` has many different classification Models to choose from, but we only have limited time and computing power.\n",
"As such, we tested many different models on the 2k Dataset and chose the 5 best performing ones for the big dataset.\n",
"\n",
"### The comparison\n",
"We won't put the comparison script in this notebook, but you can find it in the ``compare_models.py`` file and try it out yourself.\n",
"### Initial Comparison\n",
"We won't put the comparison script in this notebook, but you can find it in the ``compare_models_2k.py`` file and try it out yourself.\n",
"There were some rules as a baseline for comparison:\n",
"- All Hyperparameters are set to default\n",
"- All iteration limits are set to 3000\n",
"- All iteration limits are set to 3000 (exception: MLPClassifier with 300, where i-limit are epochs instead of iterations )\n",
"- All ``random_state``s are set to 0\n",
"\n",
"![Comparison Image](./compare_models_2k.png)"
"Running all models with that configuration yields the following weighted F1-Scores (results as seen in the ``games_march2025_cleaned_2k_i3k`` folder): \n",
"\n",
"![Comparison Image 2k](./compare_models_2k.png)\n",
"\n",
"If we also compare Micro/Macro values, we see that all models have a much lower Macro-F1 than Micro/Weighted-F1. That is because the 2k Dataset does not contain enough datapoints for every class (test data for 2 classes is 0), so we should proceed to the 10k Dataset before making major choices.\n",
"\n",
"![Comparison Image 2k Micro/Macro/Weighted](./compare_models_2k_3.png)\n",
"\n",
"The 10 best performing models which will run on the 10k Dataset with the same rules as before:\n",
"1. NearestCentroid\n",
"2. Perceptron\n",
"3. PassiveAggressiveClassifier\n",
"4. LinearSVC\n",
"5. SDGClassifer\n",
"6. HistGradientBoostingClassifier\n",
"7. MLPClassifier\n",
"8. RidgeClassifier\n",
"9. GradientBoostingClassifier\n",
"10. LinearDiscriminationAnalysis\n",
"\n",
"![Comparison Image 10k](./compare_models_10k.png)\n",
"\n",
"We can also compare these models between datasets, to see if a bigger dataset always improves the performance.\n",
"\n",
"![Comparison Image between 2k and 10k](./compare_models_2k_10k.png)\n",
"\n",
"The final contenders are:\n",
"1.\n",
"2.\n",
"3.\n",
"4.\n",
"5.\n",
"\n",
"..."
]
},
{