This page is dedicated to analyzation of different ML model I had to make in Data Mining and Machine Learning course at CMI.
<aside> 💡 This is the evaluation metrics of different ML model fitted on UCI Banking Dataset
train_whtd_rcl | test_whtd_rcl | test_acc | test_prec | test_roc | time_to_train (ns) | model_size (KB) | |
---|---|---|---|---|---|---|---|
DTclf | 0.901719 | 0.902602 | 0.902602 | 0.643432 | 0.631061 | 6.80573e+07 | 5.63574 |
gnb | 0.870637 | 0.869002 | 0.869002 | 0.412993 | 0.671698 | 3.07792e+07 | 2.31738 |
mnb | 0.889071 | 0.891881 | 0.891881 | 0.52505 | 0.636322 | 1.21876e+07 | 2.33496 |
bnb | 0.876651 | 0.878938 | 0.878938 | 0.443425 | 0.643411 | 2.26029e+07 | 2.35254 |
DT Classifier
Gaussian NB
Multinomial NB
Binomial NB
</aside>
<aside> 💡 This is the evaluation metrics of different ML model fitted on Bollywood Movies Dataset
train_whtd_rcl | test_whtd_rcl | test_acc | test_prec | test_roc | time_to_train (ns) | model_size (KB) | |
---|---|---|---|---|---|---|---|
DTclf | 0.818125 | 0.805882 | 0.805882 | 0.705882 | 0.714669 | 3.8615e+06 | 3.63965 |
gnb | 0.784241 | 0.785294 | 0.785294 | 0.818182 | 0.631422 | 2.5081e+06 | 1.68262 |
mnb | 0.749677 | 0.758824 | 0.758824 | 0.571429 | 0.682148 | 1.6352e+06 | 1.7002 |
bnb | 0.722374 | 0.761765 | 0.761765 | 0.614035 | 0.641455 | 2.5861e+06 | 1.71777 |
DT Classifier
Gaussian NB
Multinomial NB
Binomial NB
</aside>