Bowles_5.3_rocks: Binary Classification: Using Penalized Linear Regression to Detect Unexploded Mines
Listing 5-4: Using ElasticNet Regression to Build a Binary (Two-Class) Classifier— rocksVMinesENetRegCV.py
- Figure 5-7: Out-of-sample classifier misclassification performance
- Figure 5-8: Out-of-sample classifier AUC performance
- Figure 5-9: Receiver operating characteristic for best performing classifier
Listing 5-5: Coefficient Trajectories for ElasticNet Trained on Rocks versus Mines Data— rocksVMinesCoefCurves.py
- Figure 5-10: Coefficient curves for ElasticNet trained on rocks versus mines data
Listing 5-6: Penalized Logistic Regression Trained on Rocks versus Mines Data— rocksVMinesGlmnet.py
- Figure 5-11: Coefficient curves for ElasticNet penalized logistic regression trained on rocks versus mines data
ipynb: $anaconda/ipynb-tasks/bowles/Bowles_5.3_rocks.ipynb