ML Engineering
Using advanced machine learning models to enhance cancer detection, with a focus on building decision tree classifiers to improve the accuracy and efficiency of diagnosis and treatment.
ML Engineering
Using advanced machine learning models to enhance cancer detection, with a focus on building decision tree classifiers to improve the accuracy and efficiency of diagnosis and treatment.
ML Engineering
Using advanced machine learning models to enhance cancer detection, with a focus on building decision tree classifiers to improve the accuracy and efficiency of diagnosis and treatment.
Project
Machine Learning
Qualitative Applied Research
Role
ML Engineering
Year
2023 - 2024
Situation
Research at Toronto Metropolitan University's computational public safety lab is dedicated to leveraging machine learning, artificial intelligence, and robotics to foster safer and more inclusive communities. Developing a breadth of models to master machine learning engineering skills is crucial for the successful deployment of robotics systems.
Task
The main objective was to develop a decision tree classifier for the detection of cancerous cells using the Wisconsin Breast Cancer dataset.
Task
The main objective was to develop a decision tree classifier for the detection of cancerous cells using the Wisconsin Breast Cancer dataset.
Task
The main objective was to develop a decision tree classifier for the detection of cancerous cells using the Wisconsin Breast Cancer dataset.
Dataset Import and Processing
Imported the breast cancer dataset, segregating it into feature sets and labels, preparing it for model training.
Dataset Import and Processing
Imported the breast cancer dataset, segregating it into feature sets and labels, preparing it for model training.
Dataset Import and Processing
Imported the breast cancer dataset, segregating it into feature sets and labels, preparing it for model training.
Model Training
Trained the decision tree classifier with the dataset, tuning it to identify patterns associated with the presence of cancerous cells.
Model Training
Trained the decision tree classifier with the dataset, tuning it to identify patterns associated with the presence of cancerous cells.
Model Training
Trained the decision tree classifier with the dataset, tuning it to identify patterns associated with the presence of cancerous cells.
Accuracy Evaluation
Evaluated the model's accuracy in categorizing cells as cancerous or non-cancerous, ensuring reliable diagnosis.
Accuracy Evaluation
Evaluated the model's accuracy in categorizing cells as cancerous or non-cancerous, ensuring reliable diagnosis.
Accuracy Evaluation
Evaluated the model's accuracy in categorizing cells as cancerous or non-cancerous, ensuring reliable diagnosis.
Decision Tree Visualization
Visualized the decision tree to elucidate the model's decision-making pathways, improving interpretability and trust in the model.
Decision Tree Visualization
Visualized the decision tree to elucidate the model's decision-making pathways, improving interpretability and trust in the model.
Decision Tree Visualization
Visualized the decision tree to elucidate the model's decision-making pathways, improving interpretability and trust in the model.
Model Optimization Loop
Implemented a for-loop to iterate over various tree depths to optimize the model's performance and accuracy.
Analysis &
Insight Synthethis
Implemented a for-loop to iterate over various tree depths to optimize the model's performance and accuracy.
Model Optimization Loop
Implemented a for-loop to iterate over various tree depths to optimize the model's performance and accuracy.
Result
Achieved a significant classifier accuracy of 92%, demonstrating the successful development of machine learning models for deployment in public safety and health sectors.
Result
Achieved a significant classifier accuracy of 92%, demonstrating the successful development of machine learning models for deployment in public safety and health sectors.
Result
Achieved a significant classifier accuracy of 92%, demonstrating the successful development of machine learning models for deployment in public safety and health sectors.
Made by
Good
Humans.
Situation
The main objective was to develop a decision tree classifier for the detection of cancerous cells using the Wisconsin Breast Cancer dataset.
Situation
The main objective was to develop a decision tree classifier for the detection of cancerous cells using the Wisconsin Breast Cancer dataset.
Situation
Research at Toronto Metropolitan University's computational public safety lab is dedicated to leveraging machine learning, artificial intelligence, and robotics to foster safer and more inclusive communities. Developing a breadth of models to master machine learning engineering skills is crucial for the successful deployment of robotics systems.
Situation
Research at Toronto Metropolitan University's computational public safety lab is dedicated to leveraging machine learning, artificial intelligence, and robotics to foster safer and more inclusive communities. Developing a breadth of models to master machine learning engineering skills is crucial for the successful deployment of robotics systems.
Situation
Research at Toronto Metropolitan University's computational public safety lab is dedicated to leveraging machine learning, artificial intelligence, and robotics to foster safer and more inclusive communities. Developing a breadth of models to master machine learning engineering skills is crucial for the successful deployment of robotics systems.
Situation
Research at Toronto Metropolitan University's computational public safety lab is dedicated to leveraging machine learning, artificial intelligence, and robotics to foster safer and more inclusive communities. Developing a breadth of models to master machine learning engineering skills is crucial for the successful deployment of robotics systems.